Methods - Ecological Integration
The summary is organized as follows:
Integrating Assessments of Fish and Wildlife Populations and Ecological Functions
Influence of Habitats on Populations and Functions
Influence of Populations on Themselves
Influence of Populations on Other Populations
Influence of Populations on Habitats and KECs
Influence of Planning Alternatives and Management Activities on Habitats
Changes through Space and Time
The scientific principals of the Multi-Species Framework assessment process call for an analysis of fish, wildlife, and their ecological functions for evaluating alternative strategies for managing natural resources in the Columbia Basin. The following sections present methods of the assessments of what we are calling the Ecological Functions Analysis. The Ecological Functions Analysis provides a method by which ecological functions of fish and wildlife can be assessed individually and jointly.
There are many possible means by which fish and wildlife populations can interact, and how such interactions can vary geographically and over time. The purpose of the integrated fish-wildlife analysis is to provide a basis for determining if and how management activities associated with basin-wide planning alternatives significantly influence ecological interactions between fish and wildlife, and the implications of such interactions on ecosystem biodiversity, productivity, and sustainability.
The components of the Ecological Functions Analysis are shown in a diagram depicting the major categories of fish-wildlife interactions that we considered (Figure III.C.1). This figure lists the following section headings representing each type of interaction.
Integrating fish and wildlife information requires coordination of methods, data sets, and terminology used to evaluate fish species, wildlife species, and their ecological functions. The first step to relate terminology used by fish and wildlife biologists is to describe habitat. Table III.C.1 lists the KECs (habitat elements) that are used in the SHP database to define wildlife habitat elements. Table III.C.2 lists EDT Level 2 Habitat Attributes for fish and shows how the fish habitat attributes relate (i.e., crosswalk) to wildlife KECs. This fundamental coordination of habitat language allows fish and wildlife biologists to describe, in a common language, how management alternatives can influence habitat.
In addition, the common language helps both wildlife and fish managers use the SHP database that depicts which KECs are potentially influenced by categories of strategies that collectively compare alternatives assessed in this report. A Management Activities database in the SHP can, in future analyses, be used by managers to help do impact assessments of management activities on KECs for wildlife and on EDT Level 2 habitat attributes for fish. For example, strategies that are described by a suite of standardized management activities (in the SHP database) can be associated with changes in all KECs (“habitat elements” in the SHP database) as depicted, to be influenced by those activities. Then, cross-referencing which fish and wildlife species are associated with the changed KECs and EDT habitat attributes, and knowing the key ecological functions these species provide in the ecosystem, provides a basis for linking alternative management strategies with ecological functions of both fish and wildlife species.
The most fundamental interactions we assessed were the influence of habitats and key environmental correlates (KECs) on populations of fish and wildlife separately, that is, how fish habitats and KECs influence fish populations, and how wildlife habitats and KECs influence wildlife populations.
For fish, analyzing the influence of fish habitats and
KECs on fish populations entailed use of the EDT model. This methodology is discussed more fully in
the section on Fish-Methods of this report.
For wildlife, analyzing the influence of wildlife habitats and KECs on wildlife populations entailed use of the Species Habitat Project (SHP) database to evaluate potential presence by 6th HUC of wildlife species given the presence of wildlife habitats and KECs. The broad-scale, basin-wide nature of the current work, however, focused on the wildlife-habitat types, but neither their structural conditions nor the presence of specific KECs. Such refinement is left to the next step in which more spatially-refined subbasin analyses are conducted.
To evaluate potential occurrence of ecological functions by 6th HUC, we linked wildlife species (predicted present based on their wildlife habitat associations) to their KEF categories to determine which functions could be provided. This database query listed the number of wildlife species (functional redundancy) for each combination of KEF category and wildlife habitat type, occurring within each 6th HUC. We used the percent of each 6th HUC occurring in each wildlife habitat type as a weighting factor for functional redundancy, for each KEF category, thereby calculating a 6th HUC-wide weighted estimate of functional redundancy for each KEF category. It was this weighted value of KEF functional redundancy that we then used for mapping KEF functional redundancy conditions for historic, current, and future states, and for mapping changes in such conditions among time periods.
Although the resolution of these maps was the 6th HUC level, we strongly suggest viewing results at larger geographic areas, such as province and basin-wide levels. Also, at the finer, subbasin scale, incorporation of KECs and structural conditions of wildlife habitats would greatly help refine this functional analysis by providing a more precise description of environmental conditions by which to predict species presence.
A number of wildlife species are associated with aquatic and riparian habitats and KECs that influence fish. Examples include most amphibian species, marine and freshwater aquatic and semi-aquatic mammals (such as whales, mink, and river otter), and many aquatic and semi-aquatic birds (such as many shorebirds, ducks, geese, and others) that feed on aquatic macroinvertebrates. For instance, the SHP database, in fact, lists 135 wildlife species that associate with some type of freshwater riparian or aquatic body, and that feed on freshwater aquatic macroinvertebrates. Other wildlife species also influenced by fish KECs include wildlife species associated with flowing streams, stream temperature, stream and lake substrates, macrophytes and submergent vegetation, and other environmental factors.
The main purpose of highlighting this type of interaction is to be able to list wildlife species potentially benefited when providing for fish habitat conditions. Ultimately, we propose depicting habitat types, habitat structures, and key environmental correlates jointly for fish and wildlife under a combined classification system.
Managers of natural resources in the Columbia Basin have
recently been discussing the need to incorporate fish and wildlife habitat
components into a common format for evaluation or assessment. We address this need by using the KECs
(habitat elements) as a basis to integrate our depiction of fish and wildlife
habitat components. The process of
combining fish habitat attributes into the list of wildlife KECs has been
started for Chinook salmon and bull trout.
Fisheries ecologists in the EWG identified 74 KECs used by various life
history stages (Table
III.C.1). This
was a pilot effort to demonstrate the feasibility of bringing fish and wildlife
habitat information together. Proposed Framework efforts include efforts to
expand this work by identifying KECs of all resident and anadromous fish
species in the Columbia Basin by various life stages.
Once the list of KECs is expanded to include additional species of fish, then managers will be able to evaluate management strategies using a common set of variables for fish and wildlife. While this is seemingly a small step forward, it allows managers to determine how proposed land management activities, under a specific planning alternative, can affect the KECs listed in Table III.C.1 and thereby influence both salmon and wildlife associated with those elements. We demonstrated this assessment approach by querying databases listing management activities (Table III.C.2) associated with a given management activities or alternative strategy. We then listed KECs influenced by those management activities, and then we identified which species of fish and wildlife are associated with those KECs. Once knowing which species are involved, the key ecological functions (KEFs) for fish and wildlife can be jointly assessed. In this way, ecosystem functional diversity and functional redundancy can be described for all vertebrate species in the basin in a common assessment.
In some cases, fish or wildlife populations can have an influence on themselves. Such influence can take the form of density-dependent demographic relations as are depicted in traditional Ricker recruitment curves or Beverton-Holt functions. The wildlife analogue to this is found in population models represented by logistic equations, in which changes in population size occur as a function of population carrying capacity. Other influences may manifest through the effects of functional roles (KEFs) on habitat attributes and conditions, such as salmon changing the substrate structure of spawning gravel and thereby altering the capacity of the environment for spawning. Although we have not specifically modeled such within-species effects, they nonetheless might prove salient in some cases, and are worth noting.
Another class of interactions between fish and wildlife populations is how organisms can affect other species directly. Examples include predator-prey relations and competition for resources or space.
One of the major ways that fish populations can directly influence wildlife populations is through wildlife predation on fish, that is, as fish serving as prey for wildlife. We analyzed this in two ways: general patterns of wildlife predation on salmon, and wildlife species-specific use of salmon carcasses as an example of a more in-depth type of wildlife population analysis that can be done. The focus here is on affects on (benefits to) wildlife.
We analyzed how each of five salmon life stages serves as potential food for wildlife species. The SHP database depicts use of salmon life stages according to combinations of the six degrees of association (strong and consistent, recurrent, indirect, rare, unknown, and no relationship) and the five salmon live stages. We tallied number of wildlife species according to combinations of association and stage. Some wildlife species may have different degrees of association with different salmon life stages, and feed on more than one salmon life stage.
In some cases, wildlife populations might directly affect fish (salmon) populations through predation. Examples may include predation on rearing and saltwater stages of salmon by Caspian terns. In many cases, actual effect on fish population size, trends, and vital rates from such predation is unknown and unstudied, although such effects are sometimes viewed as important.
Recently, a comprehensive effort was undertaken to determine what wildlife species in Oregon and Washington have a relationship with salmon. A literature review conducted on this topic indicated a general lack of information on the relationship between the 605 species of wildlife that occur in the region and their use of salmon. Wildlife and fish species experts were contacted to address this lack of published knowledge about salmon use by wildlife. These experts were asked to address use of the five life stages of salmon as providing direct or indirect forage for wildlife species occurring in terrestrial, freshwater, estuarine, or marine environments. The life stages include incubation (eggs and alevin), freshwater rearing (fry, fingerling, parr), saltwater (smolts, immature adults, adults), spawning (adults), and carcasses. The strength of the relationships were also identified and classified as: strong and consistent, recurrent, indirect, rare, unknown, and no relationship. The results from the comprehensive effort are reported in Cederholm et al. (2000).
Future evaluation of fish and wildlife interactions will build on the relationships documented in Cederholm et al. (2000). We anticipate that future reports will discuss: disease transmission between fish and wildlife; fish-fish interactions (e.g., salmon subject to predation by small mouth bass) that could influence wildlife; fish habitat influences on wildlife; wildlife functions that influence habitat of fish; and wildlife functions that are affected by salmon predation.
Another type of interaction influence is that of how the ecological roles of organisms might alter KECs and habitat elements of other species. This includes how fish can alter KECs for other fish, and how wildlife can alter KECs for fish. Other combinations are also possible, but we considered these as the most salient for broad-scale interpretation.
The behavior of some fish might influence the habitats and KECs of other fish species. One example is how bottom-dwelling and –feeding fish such as carp can roil the substrate, reducing capability of the environment to support other organisms such as rooted submergent vegetation along with the attendant aquatic macroinvertebrates such vegetation can support. In turn, other invertebrate-feeding fish species may become reduced as well. Bottom roiling can also directly change the physical texture, potentially reducing spawning or feeding substrates for other species. Other fish-KEC interactions also likely occur.
In some cases, the ecological roles of wildlife organisms can influence habitat elements and KECs for fish. We are exploring this type of relation in greater detail, as it may provide to be a salient basis for an integrated approach to fish and wildlife habitat management.
One example is how some wildlife species might change the riparian and aquatic environments, altering specific KECs for fish such as salmon. For instance, American beaver, nutria, and several other wildlife species have the ecological role of creating aquatic structures, such as dams and lodges, which can alter stream flow and change stream morphology and stream substrates. In particular such changes might serve to alter several KECs of importance to salmon, notably degree of gravel embeddedness, temperature spatial variation, variation in channel width, and daily variation in stream flow rates. These are fish KECs recognized in the EDT model as of particular importance to salmon. A number of other wildlife KEFs and relations to fish KECs can be identified using the SHP database.
Although our broad-scale modeling does not yet quantitatively integrate such wildlife influences on fish KECs, our analysis sets the stage for such consideration and analysis at finer levels of spatial resolution. To this end, we have crafted an operational prototype of a Bayesian belief network to exemplify how such effects can be modeled, using the example discussed above (Figure III.C.2).
The major way in which management activities can influence fish and wildlife, as considered in this report, is through their effects on fish and wildlife habitats and KECs. Such effects can be depicted by using the SHP databases and EDT models. For example, we listed the wildlife KECs pertaining to species that can have major influence on fish KECs, and then determined the set of management activity categories that could influence such wildlife KECs. In this way, we can provide general information on which categories of management activities might have the greatest influence on the largest number of wildlife and fish KECs that, in turn, can have various interaction influences.
Finally, all the above influences and interactions should be viewed as potentially changing through space and time. Changes through space entails knowing how conditions in one location directly or indirectly cause changes in other locations, such as downstream effects of upstream changes in upland and riparian vegetation cover. At present, our Framework analysis deals with such effects either not at all, as with our wildlife habitat and functional analyses, or only indirectly, as with the EDT modeling analyses.
Modeling time-dynamic changes is difficult and for this broad-scale Framework we have focused on three time periods: the historic condition (roughly indicating potential historic conditions), the current condition, and future potential conditions under each of the three scenarios we explored. As the Framework is applied to more local scales, analyses should be run to determine some transition periods among these three major conditions as well.