AQUATIC GAP

An Overview of the Data Developed for the Missouri Aquatic GAP Project and an Example of How it Is Being Used for Conservation Planning

Scott P. Sowa¹ , Gust M. Annis¹ , David D. Diamond¹ , Dennis Figg² , Michael E. Morey¹ , and Timothy Nigh²

¹Missouri Resource Assessment Partnership, University of Missouri, Columbia

²Missouri Department of Conservation, Jefferson City, Missouri

At the beginning of the Missouri Aquatic GAP Project, my coworkers and I at the Missouri Resources Assessment Partnership (MoRAP) expected that conservation gaps would be the norm and not the exception. Consequently, from the start we focused on compiling and producing data that would assist planners and managers with developing conservation plans for filling those gaps. These ambitions have recently become a reality when the Missouri Department of Conservation began using our data as the core decision support system for developing a statewide conservation plan for conserving freshwater biodiversity.

Before discussing the specific data we compiled or developed for the Missouri Aquatic GAP Project, we believe it necessary to provide an overview of conservation planning. This overview will provide a general context that will more clearly illustrate why we developed each geospatial data layer. Margules and Pressey (2000) and Groves (2003) both provide excellent overviews of conservation planning, and we essentially cover the most basic elements discussed by these authors in our review of the topic.

The first step in conservation planning is to establish a goal expressing the focus of the effort. This should not be confused with the quantitative conservation goals that are established when devising a specific conservation strategy (see below). Goals pertaining to biodiversity conservation have been variously described, but all have in common the conservation and restoration of the processes that generate or sustain biodiversity.

Once a goal has been established, the fundamental principles, theories, and assumptions that must be considered in order to achieve this goal must be identified. These generally pertain to basic ecological or conservation principles and theories that will be used to guide the development of a conservation strategy for achieving the overall goal.

Because conservation planning is a geographical exercise, the next step in the process involves selecting a suitable geographic framework. More specifically, this involves selecting, defining, and mapping planning regions and assessment units. A planning region refers to the area for which the conservation plan will be developed. It defines the spatial extent of the planning effort(s). Assessment units are geographic subunits of the planning region. These units define the spatial grain of analysis and represent those units among which relative quantitative or qualitative comparisons will be made in order to select specific geographic locations as priorities for conservation. Planning regions and assessment units can be variously defined and should be hierarchical in nature to allow for multiscale assessment and planning (Wiens 1989). Boundaries could be based on sociopolitical boundaries (e.g., nations, states, counties, townships), regular grids (e.g., UTM zones or EPA EMAP hexagons), or ecologically defined units (e.g., watersheds or ecoregions). Since biodiversity does not follow sociopolitical boundaries or regular grids, whenever possible planning regions and assessment units should be based on ecologically defined boundaries, since these boundaries provide a more informative ecological context (Bailey 1995, Omernik 1995, Leslie et al. 1996, Higgins 2003).

Next, because it is impossible to directly measure or map biodiversity, surrogate targets for conservation must be identified and mapped (Margules and Pressey 2000, Noss 2004). For the terrestrial component of GAP these surrogates generally include plant communities or vegetation types and vertebrate species (Scott et al. 1991). The assumption here is that by taking measures to conserve these surrogates we are in fact taking measures to also conserve those unmapped or unmappable elements of biodiversity. Because different targets often lead to different answers on which locations should be a priority for conservation, it is generally more effective to use a variety of targets ( Kirkpatrick and Brown 1994, Noss 2004, Diamond et al. in press). Also, because biological survey data are often incomplete, biased, or completely lacking, abiotic targets (e.g., ecosystems, landscapes, or habitats), which are usually easier to map, are often considered as targets (Belbin 1993, Nicholls et al. 1998, Noss et al. 2002, Noss 2004). Angermeier and Schlosser (1995) and Noss (2004) provide excellent discussions on the reasons for using both biotic and abiotic surrogates. Also, a study by Kirkpatrick and Brown (1994) revealed that using both biotic and abiotic targets would likely be the most successful approach to representing the range of biodiversity within a planning region.

Once planning regions, assessment units, and conservation targets have been identified and mapped, an overall conservation strategy for selecting priority areas within the planning region must be established. Unfortunately, there are no detailed guidelines, and even when there is some guidance (e.g., biogeography theory, population viability analysis, or metapopulation theory) the data needed for these more detailed evaluations are usually lacking (Margules and Pressey 2000, Groves 2003). Expert opinion will therefore often play a major role in developing the overall conservation strategy.

In addition to establishing a general conservation strategy, quantitative and/or qualitative assessment criteria that will be used to make relative comparisons among assessment units must also be established. These criteria include measures of relative significance or irreplaceability, condition, future threats, costs, and opportunities, which guide the selection of one particular assessment unit over another (Groves 2003). These criteria should also be based upon the previously established fundamental principles, theories, and assumptions.

Examples include

Significance/irreplaceability : species richness, number or percent of endemic species, diversity of habitats, presence of unique habitats, species, communities, or processes

Condition : percent urban or agriculture, road density, degree of fragmentation, extent of channelization, degree of hydrologic modification, mine density, etc.

Future threat : recent or projected population trends, potential for future extractive uses

Costs : acquisition cost, restoration cost, loss of socioeconomic benefits

Opportunities : leveraging of funds or cooperation among stakeholders, local interest or involvement, ability to receive federal, state, or local funding

After addressing the issues discussed above, the next step involves selecting priority locations within the planning region(s).

Since conservation planning is a geographical exercise, it is no surprise that Geographical Information Systems (GIS) are an invaluable tool. However, because not all of the essential data are in a geospatial format, and because much of the available data often lack the necessary detail, expert knowledge must often be incorporated into the planning process. The GIS data provide a more objective, spatially explicit, and comprehensive view of the planning region, while the experts may provide additional and more detailed information for certain locations.

Conservation planning is also a logistical exercise, and once priority areas have been identified, much work remains to be done. Many questions have to be addressed, such as: Who owns the land within and around each priority area? What are the critical structural features, functional processes, and species or communities of concern within each priority area? How are we going to eliminate or minimize threats? When should conservation actions be taken, immediately or is there time? Why was each priority area selected, and why is one more “important” than another? Answering these questions is often more difficult than building the geospatial data sets and associated tools used to select priority areas. However, not addressing these important questions could lead to failure in our efforts to conserve biodiversity (Margules and Pressey 2000). Once these logistical questions have been answered, then on-the-ground conservation actions can be taken. Monitoring programs must also be established to ensure that conservation efforts are successful and to signal when and possibly how management actions should be modified. Because of the complexity and dynamic nature of ecosystems, adaptive management will be key to long-term conservation of biodiversity (Leslie et al. 1996).

So, what does this abbreviated overview of conservation planning have to do with the Missouri Aquatic GAP Project? Well, in order to adequately assess gaps in biodiversity conservation we must first identify what constitutes a gap and the only way to do this is to develop criteria for what constitutes “effective” conservation. These very criteria are established in the conservation planning process. Building on the solid foundation of the terrestrial component of GAP and going through the above process were the two most influential factors that guided the decisions we faced about the data to be compiled or developed as well as the overall approach to the Missouri Aquatic GAP Project.

The Data

The following overview of the geospatial data developed for the Missouri Aquatic GAP Project explains why and how these data were developed as a precursor to the conservation planning case study that comes later. The process for data development has four steps that are described in detail in the following sections:

1. Classify and map relatively distinct riverine ecosystems at multiple spatial scales.

2. Develop predictive distribution maps for each of the fish, mussel, and crayfish species of Missouri.

3. Develop local, watershed, and upstream riparian stewardship statistics for each stream segment within Missouri.

4. Develop or assemble geospatial data on anthropogenic threats or stressors necessary to quantitatively or qualitatively account for the current conservation status of each ecosystem unit.

Step 1: Classifying riverine ecosystems

Purpose:

• Provide the ecological and evolutionary context necessary for making truly relative comparisons among two or more locations.
• Provide an ecologically meaningful geographic framework for conservation planning (i.e., planning regions and assessment units).
• Provide surrogate abiotic conservation targets to complement biotic targets.
• Account for broader ecosystem or evolutionary processes that are often not considered with the use of species data alone.
• Account for poorly known or unknown ecosystem processes, aquatic assemblages, and organisms.
• Provide a geographic template and predictor variables for developing predictive species distribution models and maps.
• Provide the necessary reductionist tool for generating inventory statistics, conducting conservation assessments, and developing conservation plans.
• Enhance our understanding of the number and spatial distribution of distinct ecosystem types and riverine assemblages.
• Enhance communication among resource professionals, legislators, and the public.

It is widely accepted that to conserve biodiversity we must conserve ecosystems (Franklin 1993, Grumbine 1994). It is also widely accepted that ecosystems can be defined at multiple spatial scales (Noss 1990, Orians 1993). Consequently, a key objective was to define and map distinct riverine ecosystems (often termed ecological units) at multiple levels. Yet, before distinct riverine ecosystems could be classified and mapped, the question “What factors make an ecosystem distinct?” needed to be answered. Ecosystems can be distinct with regard to their structure, function, or composition (Noss 1990). Structural features in riverine ecosystems include factors such as depth, velocity, substrate, or the presence and relative abundance of habitat types. Functional properties include factors such as flow regime, thermal regime, sediment budgets, energy sources, and energy budgets. Composition can refer to either abiotic (e.g., habitat types) or biotic factors (e.g., species). While both are important, our focus here will be on biological composition, which can be further subdivided into ecological composition (e.g., physiological tolerances, reproductive strategies, foraging strategies, etc.) or taxonomic composition (e.g., distinct species or phylogenies) (Angermeier and Schlosser 1995). Geographic variation in ecological composition is generally closely associated with geographic variation in ecosystem structure and function. For instance, fish species found in streams draining the Central Plains of northern Missouri generally have higher physiological tolerances for low dissolved oxygen and high temperatures than species restricted to the Ozarks, which corresponds to the prevalence of such conditions within the Central Plains (Pflieger 1971, Matthews 1987, Smale and Rabeni 1995a, 1995b). Differences in taxonomic composition, not related to differences in ecological composition, are typically the result of differences in evolutionary history between locations (Mayr 1963). For instance, differences among biological assemblages are found on islands despite the physiographic similarity of the islands.

Considering the above, a more specific objective was to identify and map riverine ecosystems that are relatively distinct with regard to ecosystem structure, function, and evolutionary history (i.e., biological composition) at multiple levels. To accomplish this, an eight-level classification hierarchy was developed in conjunction with The Nature Conservancy’s Freshwater Initiative (Higgins 2003) (Figure 1). These eight geographically dependent and hierarchically nested levels (described next) were either empirically delineated using biological data or delineated in a top-down fashion using landscape and stream features (e.g., drainage boundaries, geology, soils, landform, stream size, gradient, etc.). These features have consistently been shown to be associated with or ultimately control structural, functional, and compositional variation in riverine ecosystems (Hynes 1975, Dunne and Leopold 1978, Matthews 1998). More specifically, levels 1-3 and 5 account for geographic variation in taxonomic or genetic-level composition resulting from distinct evolutionary histories, while levels 4 and 6-8 account for geographic variation in ecosystem structure, function, and ecological composition of riverine assemblages. The most succinct way to think about the hierarchy is that it represents a merger between the different approaches taken by biogeographers and physical scientists for tesselating the landscape into distinct geographic units.

Figure 1. Maps of Missouri showing four of the eight levels of the MoRAP aquatic ecological classification hierarchy. Maps of the upper three levels (Zone, Subzone, and Region) of the hierarchy are provided in Maxwell et al. (1995). Level 8 of the hierarchy is also not shown since the distinct units within this level (e.g., riffles, pools, glides) cannot be mapped within a GIS at a scale of 1:100,000.

Levels 1 – 3: Zone, Subzone, and Region

The upper three levels of the hierarchy are largely zoogeographic strata representing geographic variation in taxonomic (family- and species-level) composition of aquatic assemblages across the landscape resulting from distinct evolutionary histories (e.g., Pacific versus Atlantic drainages). For these three levels we adopted the ecological units delineated by Maxwell et al. (1995) who used existing literature and data, expert opinion, and maps of North American aquatic zoogeography (primarily broad family-level patterns for fish and also unique aquatic communities) to delineate each of the geographic units in their hierarchy. More recent quantitative analyses of family-level faunal similarities for fishes conducted by Matthews (1998) provide additional empirical support for the upper levels of the Maxwell et al. (1995) hierarchy. The ecological context provided by these first three levels may seem of little value; however, such global or subcontinental perspectives are critically important for research and conservation (see pp. 261-262 in Matthews 1998). For instance, the physiographic similarities along the boundary of the Mississippi and Atlantic drainages often produce ecologically similar (i.e., functional composition) riverine assemblages within the smaller streams draining either side of this boundary, as Angermeier and Winston (1998) and Angermeier et al. (2000) found in Virginia. However, from a species composition or phylogenetic standpoint, these ecologically similar assemblages are quite different as a result of their distinct evolutionary histories (Angermeier and Winston 1998, Angermeier et al. 2000). Such information is especially important for those states that straddle these two drainages, such as Georgia, Maryland, New York, North Carolina, Pennsylvania, Tennessee, Virginia, and West Virginia, since simple richness or diversity measures not placed within this broad ecological context would fail to identify, separate, and thus conserve distinctive components of biodiversity. The importance of this broader context also holds for those states that straddle the continental divide or any of the major drainage systems of the United States (e.g., Mississippi Drainage vs. Great Lakes or Rio Grande Drainage).

Level 4: Aquatic Subregions

Aquatic Subregions are physiographic or ecoregional substrata of Regions and thus account for differences in the ecological composition of riverine assemblages resulting from geographic variation in ecosystem structure and function. However, the boundaries between Subregions follow major drainage divides to account for drainage-specific evolutionary histories in subsequent levels of the hierarchy. The three Aquatic Subregions that cover Missouri (i.e., Central Plains, Ozarks, and Mississippi Alluvial Basin) largely correspond to the three major aquatic faunal regions of Missouri described by Pflieger (1989). Pflieger (1989) used a species distributional limit analysis and multivariate analyses of fish community data to empirically define these three major faunal regions. Subsequent studies examining macroinvertebrate assemblages have provided additional empirical evidence that these Subregions are necessary strata to account for biophysical variation in Missouri’s riverine ecosystems (Pflieger 1996, Rabeni et al. 1997, Rabeni and Doisy 2000). Each Subregion contains streams with relatively distinct structural features, functional processes, and aquatic assemblages in terms of both taxonomic and ecological composition.

Level 5: Ecological Drainage Units

Level 5 of the hierarchy, Ecological Drainage Units (EDUs), accounts for differences in taxonomic composition (Figure 2). An initial set of EDUs was empirically defined by grouping USGS 8-digit hydrologic units (HUs) with relatively similar fish assemblages, based on the results of multivariate analyses of fish community data (Nonmetric Multidimensional Scaling, Principal Components Analysis, and Cluster Analysis). We then used collection records for three other taxa (crayfish, mussels, and snails) to further examine faunal similarities among the major drainages within each Subregion and refined the boundaries of this draft set of EDUs when necessary. Spatial biases and other problems with the data prohibited including these taxa in the multivariate analyses. In only one instance were the draft boundaries altered. Within the Ozark Aquatic Subregion the subdrainages of the Osage and Gasconade basins consistently grouped together using the methods described above. However, a more general assessment using Jacaard similarity coefficients suggested the need to separate these two drainages. Using just fish community data, the Jacaard similarity coefficient among these two drainages is 86, while when using combined data for crayfish, mussels, and snails the similarity coefficient drops to only 56.

Figure 2. Map of the Ecological Drainage Units (EDUs) of Missouri

Level 6: Aquatic Ecological System Types

To account for finer-resolution variation in ecological composition we used multivariate cluster analysis of quantitative landscape data to group small- and large-river watersheds into distinct Aquatic Ecological System Types (AES-Types). AES-Types represent watersheds or subdrainages that are approximately 100 to 600 mi² with relatively distinct (local and overall watershed) combinations of geology, soils, landform, and groundwater influence (Figure 3). We determined the number of distinct types by examining relativized overlay plots of the cubic clustering criterion, pseudo F-statistic, and the overall R-square as the number of clusters was increased (Calinski and Harabasz 1974, Sarle 1983). Plotting these criteria against the number of clusters and then determining where these three criteria are simultaneously maximized provides a good indication of the number of distinct clusters within the overall data set (Calinski and Harabasz 1974, Sarle 1983, Milligan and Cooper 1985, SAS 1990, Salvador and Chan 2003). Thirty-eight AES-Types were identified for Missouri with this method.

Figure 3. Map of the Aquatic Ecological Systems (AESs) and Types (AES-Types) for Missouri .

AES-Types often initially generate confusion simply because the words or acronym used to name them are unfamiliar. AES-Types are just “habitat types” at a much broader scale than most aquatic ecologists are familiar with. For example, a riffle is a habitat type, yet there are literally millions of individual riffles that occupy the landscape. Each riffle is a spatially distinct habitat; however, they all fall under the same habitat type with relatively similar structural features, functional processes, and ecologically defined assemblages. The same holds true for AES-Types. Each individual AES is a spatially distinct macrohabitat, however, all individual AESs that are structurally and functionally similar fall under the same AES-Type.

Level 7: Valley Segment Types

In Level 7 of the hierarchy Valley Segment Types (VSTs) are defined and mapped to account for longitudinal and other linear variation in ecosystem structure and function that is so prevalent in lotic environments (Figure 4 ). Stream segments within the 1:100,000 USGS/EPA National Hydrography Dataset were attributed according to various categories of stream size, flow, gradient, temperature, and geology through which they flow, and also the position of the segment within the larger drainage network. These variables have been consistently shown to be associated with geographic variation in assemblage composition (Moyle and Cech 1988, Pflieger 1989, Osborne and Wiley 1992, Allan 1995, Seelbach et al. 1997, Matthews 1998). Each distinct combination of variable attributes represents a distinct VST. Stream size classes (i.e., headwater, creek, small river, large river, and great river) are based on those of Pflieger (1989), which were empirically derived with multivariate analyses and prevalence indices. As in the level 6 AESs, VSTs may seem foreign to some, yet if they are simply viewed as habitat types the confusion is removed. Each individual valley segment is a spatially distinct habitat, but valley segments of the same size, temperature, flow, gradient, etc. all fall under the same VST.

Figure 4. Map showing examples of several different Valley Segment Types (VSTs) within a small watershed of the Meramec EDU.

Level 8: Habitat Types

Units of the final level of the hierarchy, Habitat Types (e.g., high-gradient riffle, lateral scour pool), are simply too small and temporally dynamic to map within a GIS across broad regions or at a scale of 1:100,000. However, we believe it is important to recognize this level of the hierarchy, since it is a widely recognized component of natural variation in riverine assemblages (Bisson et al. 1982, Frissell et al. 1986, Peterson 1996, Peterson and Rabeni 2001).

Step 2: Develop predictive distribution maps for fish, mussels, and crayfish

Purpose:

• Only 0.03% of the stream miles in Missouri have been sampled, and much of this data is spatially and temporally biased. Predicted distribution maps provide us with spatially comprehensive biological data at the finest level of our gap analysis (individual stream segment), which is a resolution that managers can comprehend and at which conservation action typically takes place.
• Since we cannot directly measure or map biodiversity, species within those taxa for which adequate sampling data is available and the associated assemblages must serve as surrogate biotic targets for biodiversity conservation, which complement the abiotic targets.
• Conservation values of society are largely biologically based. The public, legislators, and even scientists can more readily comprehend and relate to biologically based assessments than other measures of biodiversity (e.g., habitat or processes).

To construct our predictive distribution models we compiled nearly 7,000 collection records for fish, mussels, and crayfish and spatially linked these records to the 12-digit USGS/NRCS Hydrologic Unit coverage for Missouri and also to the Valley Segment GIS coverage. Range maps were produced for each of the 315 species, sent out for professional review, and modified as needed. Then we used Decision Tree Analyses to construct predictive distribution models for each species. Ultimately, a total of 571 models were developed to construct reach-specific predictive distribution maps for the 315 species. The resulting maps were merged into a single hyperdistribution (Figure 5), which is related to a database containing information on the conservation status, ecological character, and endemism level of each species.

Figure 5. Map of predicted species richness for fish, mussels, and crayfish. This map reflects resource potential and not present-day richness since human disturbances were not included in the models.

Users can select an individual stream segment within the Valley Segment coverage and generate a list of those species (and associated information) predicted to occur in that segment under relatively undisturbed conditions (anthropogenic stressors were not or could not be accounted for). An accuracy assessment was conducted for each taxonomic group using independent data. Commission errors, averaged across all three taxa, were relatively high (55%), while omission errors were relatively low (9%). We believe these accuracy statistics can be improved by incorporating watershed variables as predictors as well as by getting more detailed temperature data for valley segments. However, it must be pointed out that this accuracy assessment is fraught with problems mainly related to the inadequacy of the independent data used to evaluate the accuracy of our models (e.g., insufficient length of stream sampled, only a single sample at a single point in time, inefficient gear, and many of the sampling sites were degraded to some degree while our models predict composition under relatively undisturbed conditions). An assessment of a handful of relatively high-quality, intensively sampled streams revealed a much lower commission error rate (35%) but also a higher omission error rate (18%).

Step 3: Develop local, watershed, and upstream riparian stewardship statistics for each
stream segment

Purpose:

• Assess representation of biotic and abiotic targets within the existing matrix of public lands.
• Assist with conservation planning by providing decision makers with information on which to base the selection of focus areas for conservation. For instance, a deciding factor between two locations might be the percentage of the watershed in public ownership (e.g., 10% vs. 50%).
• Assist with conservation planning by providing decision makers with information on who owns the stream segment(s) under consideration as well as the percentage of watershed or upstream riparian ownership by each agency or organization.

The GAP stewardship coverage for Missouri was used in conjunction with the Valley Segment coverage to identify stream segments flowing through public lands. A special Arc Macro Language (AML) program was used to identify only those segments that have the majority of their length ( > 51%) within public lands (Figure 6). Each segment flowing through public land is further classified according to the GAP stewardship categories (1-4) and the specific owner. Another AML was used to calculate the percentage of each segment’s watershed and upstream riparian area in public ownership by GAP stewardship category and owner (Figure 6). Because the watersheds for many of the stream segments within Missouri extend beyond the state, the stewardship coverages for the neighboring states of Iowa, Kansas, and Nebraska were merged with that of Missouri. With these attributes users can now select any of the more than 170,000 individual stream segments within Missouri and see which segments are flowing through public lands, who owns which segments, and what percentage of the overall watershed and upstream riparian area is within public ownership, by either GAP stewardship category or owner.

Figure 6. Maps showing, a) stream segments with most of their length in public land, classified by GAP Stewardship categories 1-4, and b) stream segments with > 50% of their upstream watershed within existing publics lands

Step 4: Develop and assemble geospatial data on threats or human stressors

Purpose:

• Because ownership does not ensure effective long-term conservation, measures must be taken to account for human stressors that might significantly impair the ecological integrity of those segments currently within public ownership.
• Assist with conservation planning by providing decision makers with
  quantitative and qualitative information that can be used to identify relatively high-quality locations in order to conserve a given conservation target.
• Assist with conservation planning by providing decision makers with
  quantitative and qualitative information that can be used to identify what factors threaten the ecological integrity of a particular priority location, and which can then be used to prioritize management objectives.
• Provide spatially explicit information on human stressors to allow resource managers to pinpoint the specific location of the stressor(s) within the drainage network or watershed.

There are a multitude of stressors that negatively affect the ecological integrity of riverine ecosystems (Allan and Flecker 1993, Richter et al. 1997). The first step in any effort to account for anthropogenic stressors is to develop a list of candidate causes (U.S. EPA 2000). Working in consultation with a team of aquatic resource professionals, a list of the principal human activities known to affect the ecological integrity of streams in Missouri was generated. Then the best available (i.e., highest resolution and most recent) geospatial data that could be found for each of these stressors were assembled (Table 1). Fortunately, and somewhat surprisingly, data were available for most stressors. However, for some, such as channelized stream segments, there were no available geospatial data, and efforts to develop a coverage of such segments using a sinuosity index proved ineffective. Most of the geospatial data were acquired from U.S. EPA and the Missouri Departments of Conservation and Natural Resources.

Using the Missouri Aquatic GAP Data for Biodiversity Conservation Planning

In fall 2001, federal legislation established a new State Wildlife Grants (SWG) program, which provides funds to state wildlife agencies for conservation of fish and wildlife species, including nongame species. I n order to continue receiving federal funds through the SWG program, Congress charged each state and territory with developing a statewide Comprehensive Wildlife Conservation Strategy (CWCS).  In Missouri , the Conservation Department (MDC) is responsible for developing the CWCS. The MDC contacted MoRAP and provided funds to develop customized GIS projects that would assist in the development of a statewide plan for conserving aquatic biodiversity. These customized GIS projects include all of the data compiled or created for the Missouri Aquatic GAP Project, as well as other pertinent geospatial data. At the same time, the MDC developed customized GIS projects for developing a statewide plan for conserving terrestrial biodiversity. Interim results of these two plans will be merged into a single CWCS for the state.

After the customized GIS projects were developed, a team of aquatic resource professionals from around Missouri was assembled. The objective of this team was to address each of the basic components of conservation planning discussed above. The team formulated the following goal: Ensure the long-term persistence of native aquatic plant and animal communities, by conserving the conditions and processes that sustain them, so people may benefit from their values in the future.

The team then identified a list of principles, theories, and assumptions that must be considered in order to achieve this goal. Many were similar to those presented above and related mainly to basic principles of stream ecology, landscape ecology, and conservation biology. However, some reflected the personal experiences of team members and the challenges they face when conserving natural resources in regions with limited public land holdings. For instance, one of the assumptions identified by the team was: “ Success will often hinge upon the participation of local stakeholders, which will often be private landowners .” In fact, the importance of private lands management for aquatic biodiversity conservation was a topic that permeated throughout the initial meetings of the team.

The MoRAP aquatic ecological classification hierarchy was adopted as the geographic framework (i.e., Planning Regions and Assessment Units) for developing the conservation plan. From this classification hierarchy the team selected AES-Types and VSTs as abiotic conservation targets. They also agreed that, in order to fully address biotic targets, a list of target species (fish, mussel, and crayfish) should be developed for each EDU. These lists were developed, and they represent species of conservation concern (i.e., global ranks: G1-G3 and state ranks: S1-S3), endemic species, and focal or characteristic species (e.g., top predators, dominant prey species, unique ecological role, etc.).

Next the team crafted a general conservation strategy. The reasoning behind each component of this strategy is best illustrated by discussing what conservation objectives the team hoped to achieve with each component. These reasons are provided in Box 1 .

The conservation strategy

• must develop separate conservation plans for each EDU (Primary Planning Regions);
• whenever possible, represent two distinct spatial occurrences/populations of each target species;
• represent at least one example of each AES-Type within each EDU;
• within each selected AES, represent at least 1 km of the dominant VSTs for each size class (headwater, creek, small river, and large river) as an interconnected complex; and
• represent a least three separate headwater VSTs.

The team then established quantitative and qualitative assessment criteria for making relative comparisons among the assessment units. Since the assessment was conducted at two spatial grains (AES and VST), there exist two different assessment units with assessment criteria developed separately for each.

AES level criteria (listed in order of importance)

• Highest predicted richness of target species
• Lowest Human Stressor Index value (also qualitatively examine individual stressors)
• Highest percentage of public ownership
• Overlap with existing conservation initiatives
• Ability to achieve connectivity among dominant VSTs across size classes
• When necessary, incorporate professional knowledge of opportunities, constraints, or human stressors not captured within the GIS projects to guide the above decisions.

VST level (listed in order of importance)

• If possible, select a complex that contains known viable populations of species of special concern.
• If possible, select the highest-quality VST complex by qualitatively evaluating the relative local and watershed condition using the full breadth of available human stressor data.
• If possible, select a VST complex that is already within the existing matrix of public lands.
• If possible, select a VST complex that overlaps with existing conservation initiatives or where local support for conservation is high.
• When necessary, incorporate professional knowledge of opportunities, constraints, or human stressors not captured within the GIS projects to guide above decisions.

The conservation strategy and assessment boils down to a five-step process:

• Use the AES selection criteria to identify one priority AES for each AES-Type within the EDU.
• Within each priority AES, use the VST selection criteria to identify a priority complex of the dominant VSTs.
• For each complex of VSTs create a map of the localized subdrainage (termed “focus area”) that specifically contains the entire interconnected complex.
• Evaluate the capture of target species.
• If necessary, select additional focus areas to capture underrepresented target species.

The team then used the conservation strategy and assessment process to develop a conservation plan for the Meramec EDU. By using the above process, all of the objectives of the conservation strategy were met with 11 focus areas (Figure 7). With the initial assessment process and selection criteria, which focus on abiotic targets (AESs and VSTs), 10 separate focus areas were selected. These 10 areas represent the broad diversity of watershed and stream types that occur throughout the Meramec EDU. Within this initial set of 10 focus areas all but five of the 103 target species were captured (Table 2). The distribution of all five of these species overlapped within the same general area of the EDU, near the confluence of the Meramec and Dry Fork Rivers. Consequently, all five of these species were captured by adding a single focus area (the Dry Fork/Upper Meramec focus area, see Figure 7).

Figure 7. Map showing the 11 Focus Areas selected for the Meramec EDU as part of the aquatic component of the Missouri Comprehensive Wildlife Conservation Strategy. The stream segments within Focus Area number 2 (Dry Fork Upper Meramec) were selected in order to capture those target species not captured in the 10 Focus Areas selected using the initial assessment and selection criteria, which focus on abiotic targets.

The final set of priority valley segments, within the 11 focus areas, constitutes 186 miles of stream. This represents 2.8% of the total stream miles within the Meramec EDU. The focus areas themselves represent an overall area of 213 mi², which is 5% of the nearly 4,000 mi² contained within the EDU. Obviously, efforts to conserve the overall ecological integrity of the Meramec EDU cannot be strictly limited to the land area and stream segments within these focus areas. In some instances the most important initial conservation action will have to occur outside of a given focus area, yet the intent of those actions will be to conserve the integrity of the particular focus area. Specific attention to, and more intensive conservation efforts within, these 11 focus areas provides an efficient and effective strategy for the long-term maintenance of relatively high-quality examples of the various ecosystem and community types that exist within this EDU.

In addition to selecting focus areas, the team provides information that can assist with the remaining logistical tasks. This information is captured within a database that can be spatially related to the resulting GIS coverage of the focus areas. Specifically, each focus area is given a name that generally corresponds with the name of the largest tributary stream, then each of the following items is documented:

• all of the agencies or organizations that own stream segments within the focus area and own portions of the overall watershed or upstream riparian area,
• the specific details of why each AES and VST complex was selected,
• any uncertainties pertaining to the selection of the AES or VST complex and if there are any alternative selections that should be further investigated,
• how these uncertainties might be overcome, such as conducting field sampling to evaluate the accuracy of the predictive models or doing site visits to determine the relative influence of a particular stressor,
• all of the management concerns within each focus area and the overall watershed,
• any critical structural features, functional processes, or natural disturbances,
• what fish, mussel, and crayfish species exist within the focus area for each stream size class, and
• any potential opportunities for cooperative management or working in conjunction with existing conservation efforts.

All of this information is critical to the remaining logistical aspects of conservation planning that must be addressed once geographic priorities have been established. Also, since work cannot be immediately initiated within all of the focus areas, there must be priorities established among the focus areas in order to develop a schedule of conservation action (Margules and Pressey 2000). For Missouri , this will initially take place within each EDU and then again from a statewide perspective. An important aspect of generating a “comprehensive” plan is that conservation is often driven by opportunity; by identifying a portfolio of priority locations, quick action can be taken when opportunities arise (Noss et al. 2002).

At present, the selection of focus areas has been completed for 13 of the 17 EDUs. The remaining EDUs will be completed by August 2004. Some of the most important things learned from this process include:

• Local experts are often humbled by the GIS data. Often, what appear to be the best places to conserve are those places that the local managers know little or nothing about. This exemplifies that the world is a big place, and we cannot expect a handful of experts to know every square inch of 4,000+ mi².
• The GIS data are often insufficient and, if solely relied upon, would often lead to poor decisions. There have been several instances where the GIS data point us to a particular location, while the local experts quickly point out that, for example, the sewage treatment facility just upstream has one of the worst spill records in the state, and fish kills occur almost on an annual basis. While the GIS data show the location of the sewage treatment facility, they do not contain this more detailed information.
• Even in the most highly altered and severely degraded landscapes there almost always exist “hidden jewels” that have somehow escaped the massive landscape transformations and other insults in neighboring watersheds. This experience has really revealed the social aspects of land use patterns described by Meyer (1995) .
• Ninety-five to 100% of the biotic targets are captured by initially only focusing on abiotic targets (AES-Types and VSTs). This is especially surprising in the Ozark Aquatic Subregion, which contains numerous local endemics with very restricted and patchy distributions. This suggests that these classification units do a good job of capturing the range of variation in stream characteristics that are partly responsible for the patchy distribution of these species.
• All of the abiotic and biotic targets can be captured within a relatively small fraction of the overall resource base. Unfortunately, the area of interest for managing these focus areas is often substantially larger and much more daunting. However, the reason priority locations were termed “focus areas” was that the streams and assemblages within each priority location are the ultimate focus of conservation action. Even when work is being conducted outside of a focus area, it should be directed at maintaining or restoring conditions within a particular focus area.
• If possible, priorities should be established at a scale that managers can understand and use (e.g., individual stream segments) in order to apply spatially explicit conservation actions. Each team of local experts has found the process much more useful than previous planning efforts that have identified relatively large areas as priorities for conservation. The managers have stated that, because we are selecting localized complexes of specific stream segments, much of the guesswork on where conservation action should be focused has been taken “out of the equation,” which will expedite conservation action.
  

Identifying Gaps in the Existing Matrix of Public Lands

Going through the above conservation planning exercise allowed us to more specifically quantify what constitutes a “gap.” Arguments about the validity of the specific criteria aside (e.g., why not three occurrences of each target species?), the value of this exercise must be viewed in a broad sense. The criteria embedded within the general conservation strategy are a significant improvement over basic species- or habitat-specific stewardship statistics (e.g., percent of each species range within GAP 1 or 2 lands), which are insufficient for quantifying the true extent of the problem since these statistics lack other important contextual information (e.g., connectivity, number of distinct populations, environmental quality).

What are the results if the criteria used to identify focus areas for the Missouri CWCS are used to assess gaps in the existing conservation network? (see Figure 8). Note: these statistics pertain to all public lands, not just those meeting criteria for GAP stewardship categories 1 and 2.

Figure 8. Maps showing,1) those individual AESs that have a least 1 km of the dominant VSTs (for all size classes) currently captured in public lands, 2) those from 1, where the dominant VSTs are captured as an interconnected complex, and 3) those from 2, that can be considered relatively viable options for long-term conservation.

How many individual AESs have at least 1 km of the dominant VSTs (for each size class) captured in existing public lands? 28

How many of these 28 have the dominant VSTs captured as an interconnected complex? 7

How many of these 7 can be considered viable (relatively undisturbed) ecosystems? 2

It is apparent from these results and Figure 8 that none of the EDUs have their full range of watershed or stream types currently captured within the existing matrix of public lands. Furthermore, none of the EDUs even come close to having two occurrences of all target species captured. From a conservation reserve standpoint, these results paint a bleak picture. However, these results should not come as a surprise, considering the fact that conservation of biodiversity, especially riverine biodiversity, has rarely been considered in the acquisition of public lands.

Currently, 7% of the total stream miles in Missouri are in public ownership, yet only a handful of watersheds meet the basic elements of our conservation strategy. Results, thus far, from the statewide conservation planning effort suggest that a reserve network using the outlined conservation strategy would encompass approximately 5-6% of the total stream miles in the state. Consequently, there are more stream miles currently in public ownership than what the conservation planning results suggest is minimally required to represent the “full range” of variation in stream ecosystem types and multiple populations of all fish, mussel, and crayfish species that occur within the state. This irony illustrates the importance of location and spatial arrangement for conserving riverine biodiversity, which heretofore has not been considered in the acquisition of conservation lands. Fortunately, the focus areas presently being identified for the Missouri CWCS serve as an important conservation blueprint to help fill the many voids within the existing conservation network.

Conclusions

The foundation provided by the terrestrial component of GAP in conjunction with an understanding of the basic elements of conservation planning were the key elements that have driven the approach taken in the Missouri Aquatic GAP Project. The data developed for the project are currently being used as the core information in a decision support system for developing a statewide freshwater biodiversity conservation plan. Going through the conservation planning process enabled those involved to more specifically define what constitutes effective conservation for a particular ecosystem and thus better define what constitutes a conservation gap. The gap analysis results are not encouraging. However, the results from the conservation planning efforts provide hope that relatively intact ecosystems still exist even in highly degraded landscapes. Results also suggest that a wide spectrum of the abiotic and biotic diversity can be represented within a relatively small portion of the total resource base, with the understanding that for riverine ecosystems the area of conservation concern is often substantially larger than the focus areas.

Selecting focus areas for conservation is the first step toward effective biodiversity conservation, and the Gap Analysis Program is providing data critical to this task. Yet, establishing geographic priorities is only one of the many steps in the overall process of achieving real conservation. Achieving the ultimate goal of conserving biodiversity will require vigilance on the part of all responsible parties, with particular attention to addressing and coordinating the remaining logistical exercises.

Box 1 : What We Are Trying to Achieve with Each Component of the General Conservation Strategy Established for the Missouri CWCS

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