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Informing Recovery Management of the Threatened Blackside Dace, Chrosomus cumberlandensis, using a Bayesian-Belief Network Model
Kevin T. McAbee, Nathan P. Nibbelink, Trisha D. Johnson, and Hayden T. Mattingly

Southeastern Naturalist, Volume 12, Special Issue 4 (2013):143–161

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143 K.T. McAbee, N.P. Nibbelink, T.D. Johnson, and H.T. Mattingly 2013 Southeastern Naturalist Vol. 12, Special Issue 4 Informing Recovery Management of the Threatened Blackside Dace, Chrosomus cumberlandensis, using a Bayesian-Belief Network Model Kevin T. McAbee1,2,*, Nathan P. Nibbelink1, Trisha D. Johnson3,4, and Hayden T. Mattingly3 Abstract - Integrated modeling frameworks allow resource managers to incorporate multiple sources of information (both data and expert judgment), acknowledge uncertainty, and make quantitative predictions about resource outcomes. To demonstrate the utility of an integrated-modeling approach for recovery planning of imperiled species, we developed a comprehensive model in the form of a Bayesian-belief network to support recovery of a federally listed stream fish, Chrosomus cumberlandensis (Blackside Dace). Our model quantitatively combined expert judgment and data from empirical studies to create a comprehensive model that is testable, transferable, and easily communicated. Sensitivity- and scenario-building analyses demonstrated that mining impacts such as elevated stream conductivity were the most influential variables affecting predicted local Blackside Dace population persistence. Our results suggest that mining impacts are a logical focal point for research and recovery actions for the species, but additional review and revision of the model are recommended. Taken as a whole, our effort enhances the current and future capacity for informed recovery-management of Blackside Dace populations. Introduction Improving the condition of an imperiled species to a more stable, protected status (i.e., recovery) fundamentally requires the explicit inclusion of science into management decision-making (Boersma et al. 2001). As with most natural resource management decisions, planning for the recovery of imperiled species must consider four primary sources of difficulty: ecological complexity; multiple sources of uncertainty; multiple, possibly competing objectives; and differing stakeholder values (Clemen 1996). These difficulties are minimized when complex ecological relationships (such as life history, demographics, environmental inputs, and human disturbances) are incorporated into a comprehensive, integrated- modeling framework (Clemen 1996, Peterson and Evans 2003). Furthermore, such an integrated modeling-framework assists recovery planning by explicitly linking research and monitoring to management (Peterson and Evans 2003). In addition to the primary sources of difficulty in decision-making, recovery planning for imperiled species, such as those protected under the US Endangered 1Warnell School of Forestry and Natural Resources, The University of Georgia, D.W. Brooks Drive, Athens, GA 30602. 2Current Address - US Fish and Wildlife Service, Utah Ecological Services Field Office, 2369 West Orton Circle, Suite 50, West Valley City, UT 84119. 3Department of Biology, Box 5063, Tennessee Technological University, Cookeville, TN 38505. 4Current Address - The Nature Conservancy, 2021 21st Avenue, Nashville, TN 37212. *Corresponding Author - Kevin_McAbee@fws.gov. Ecology and Conservation of the Threatened Blackside Dace, Chrosomus cumberlandensis 2013 Southeastern Naturalist 12(Special Issue 4):143–161 K.T. McAbee, N.P. Nibbelink, T.D. Johnson, and H.T. Mattingly 2013 Southeastern Naturalist 144 Vol. 12, Special Issue 4 Species Act of 1973 (ESA), presents unique management challenges (Marcot and Molina 2007). Imperiled species are often highly specialized, narrowly dispersed, newly classified, or very sensitive, attributes which make them difficult to detect in the field or to use in experimental studies (Flather and Sieg 2007). These obstacles are associated with data deficiencies and high uncertainty about system dynamics (Marcot and Molina 2007). As a result, managers often rely on expert judgment when initially creating recovery plans. Because these judgments can become ingrained as fact rather than be treated as valuable yet untested hypotheses, it is imperative to re-evaluate recovery goals over time. To this end, we suggest that recovery management for many imperiled species would benefit from the use of a predictive model that uses an integrated-modeling approach that incorporates multiple sources of information. Modeling frameworks such as visual diagrams and probabilistic models allow managers to incorporate multiple sources of information (e.g., data, empirical models, expert judgment), acknowledge uncertainty, and make quantitative predictions of outcomes under various management alternatives (Blomquist et al. 2010, Clemen 1996, Peterson and Evans 2003). Here, we present an integratedmodeling framework developed specifically to support recovery of the federally listed stream fish, Chrosomus cumberlandensis (Starnes and Starnes) (Blackside Dace). Our framework is formatted as a Bayesian-belief network (BBN), a probabilistic graphical model that describes conditional dependencies between variables. The BBN includes a description of system components (e.g., biological and physical variables) thought to most influence recovery of Blackside Dace through ecological relationships. One important aspect of our work was the collaborative process used to develop the BBN, a process that mimicked recovery-team planning commonly used under the ESA (the most common form of imperiled species management in the United States). Our study had two primary objectives: (1) to develop a functional model (BBN) incorporating the best empirical data and expert knowledge available, and (2) to evaluate the BBN’s utility through sensitivity analysis and scenario building. An initial BBN of Blackside Dace system dynamics was created to document our current understanding of system structure. A sensitivity analysis was then used to assess the relative influence of system components on management outcomes. Finally, several land-management scenarios were simulated to predict Blackside Dace population response to various combinations of ecological stressors. Methods Blackside Dace The Blackside Dace is a small-bodied, cyprinid fish species endemic to tributary streams in the Upper Cumberland River drainage of southeastern Kentucky and northeastern Tennessee (Black et al. 2013a [this issue]; Etnier and Starnes 2001; Starnes and Starnes 1978, Starnes and Starnes 1981). The species was federally listed as threatened in 1987 by the US Fish and Wildlife Service (USFWS) 145 K.T. McAbee, N.P. Nibbelink, T.D. Johnson, and H.T. Mattingly 2013 Southeastern Naturalist Vol. 12, Special Issue 4 because of limited distribution, extirpation of local populations (O’Bara 1985), and threats to habitat integrity from forestry and mining activities (USFWS 1987). A number of Blackside Dace research studies and other efforts related to recovery have been completed since the species was listed (McAbee 2008; see Supplemental Appendix 1, available online at https://www.eaglehill.us/ SENAonline/suppl-files/s12-Sp4-1040f-McAbee-s1, and, for BioOne subscribers, at http://dx.doi.org/10.1656/S1040f.s1). For example, the Tennessee Wildlife Resources Agency (TWRA) and other interested parties began developing the Northern Cumberlands Forest Resources Habitat Conservation Plan (NCFRHCP) in 2006 to guide activities associated with timber harvest on wildlife management areas in Tennessee (Blomquist et al. 2010, NCFRHCP 2012). The Blackside Dace was one of several imperiled species selected for coverage by the NCFRHCP (Blomquist et al. 2010, NCFRHCP 2012). To assist in developing required habitat conservation plan (HCP) components for Blackside Dace, we composed an up-to-date species account from the published literature and other sources. Next, we developed a survey questionnaire to administer to Blackside Dace experts, with several questions specifically related to HCP components (e.g., monitoring). An “expert” was defined as a person whose knowledge and experience with Blackside Dace clearly exceeded that of the average fish biologist in Kentucky or Tennessee. Typically, such experience was acquired through field research or population monitoring of Blackside Dace. The main purpose of the survey questionnaire was to complement the literature review with expert knowledge that might not be available in the literature. Fifteen individuals completed the questionnaire for Blackside Dace. The species account and survey results are presented in Supplemental Appendix 1. Modeling approach Modeling complex interactions and incorporating multiple sources of information necessitates a logical framework in which to easily communicate relationships and results (Clemen 1996). We chose to structure the Blackside Dace model in a specialized influence diagram, a BBN. BBNs are directed, acyclic graphs used to represent the relationship between variables in a probabilistic manner (Pollino et al. 2007), and they are increasingly being used for natural resources problems, such as water planning (Cain 2001), land management (Rieman et al. 2001, Martin et al. 2005), habitat assessment (McNay et al. 2006, Smith et al. 2007), population viability (Marcot et al. 2001, Steventon et al. 2006), and decision analysis (Conroy et al. 2008, Marcot et al. 2006a, Peterson et al. 2013, Pollino et al. 2007). In wildlife management, BBNs are used to describe the cascade of influences that shape management outcomes of interest (Marcot et al. 2006b). In order to incorporate the most comprehensive and current ecological knowledge, a working group of 11 Blackside Dace experts was assembled to assist in construction of the BBN. This diverse group of scientists represented federal and state agencies, research universities, and non-profit organizations, and provided K.T. McAbee, N.P. Nibbelink, T.D. Johnson, and H.T. Mattingly 2013 Southeastern Naturalist 146 Vol. 12, Special Issue 4 similarly diverse skills, knowledge, and experience. More than half of the working group members (7 of 11, or 64%) had previously completed the Blackside Dace survey questionnaire described above. Involving experts offered many benefits to the model-development process, including incorporating as much knowledge as possible, tailoring the model to end users, and promoting a sense of teamwork within the management process. Defining and refining the elements of the model are important for model transparency, utility and effectiveness (Clemen 1996). Elements within our Blackside Dace model were first defined using scientific support and empirical data, with the goal of including dynamic, explicit science within the plan (Boersma et al. 2001). Where empirical data were not available, elements were defined using the expert judgment of working group members. Model refinement was performed throughout the model development process. Our overall modeling approach is illustrated in Figure 1. Figure 1. Sequence of actions taken by the authors to develop and refine a Bayesian-belief network to inform recovery management of Blackside Dace, Chrosomus cumberlandensis. BBN = Bayesian-belief network. 147 K.T. McAbee, N.P. Nibbelink, T.D. Johnson, and H.T. Mattingly 2013 Southeastern Naturalist Vol. 12, Special Issue 4 Development of initial BBN We used the software Netica (Norsys 2007) to develop and refine our Blackside Dace BBN. Following Marcot et al. (2006b), the first step of BBN development is the creation of an influence diagram representing ecological linkages that affect the species of interest. To derive these linkages, we compiled the species account and analyzed survey results (Supplemental Appendix 1), thereby establishing a scientific foundation for the model. As discussed above, survey questions were designed primarily to guide development of NCRFHCP components, but survey responses also provided BBN facilitators with sufficient information to draft an influence diagram. In December 2007, the draft influence diagram was presented to the Blackside Dace working group, where it was discussed and modified until consensus was reached. The updated influence diagram was developed and ratified through an open dialogue concerning future applications of the model that included the exchange of knowledge (data, conditions, and trends) and respectful debate of ecological hypotheses. Therefore, the resulting ecological causal web was supported by the group as a whole, but may not represent the exact views of any individual. To simplify the model, modifications to the diagram were made by removing variables that had little or no support in published literature, were not present across the complete range of the species, or had high correlation to a retained model variable within the same component class (detailed in McAbee 2008). When finalized, the group structured the influence diagram with four types of model components representing: (1) human and environmental inputs, (2) key ecological correlates of (2a) associated upland habitat conditions and (2b) stream habitat conditions, (3) biological (life-history) effects, and (4) population response (Fig. 2). The influence diagram contained a set of model components and the linkages between them, but in order to convert this diagram to a BBN, each component was partitioned into a set of discrete states (Cain 2001, Marcot et al. 2006b, Pollino et al. 2007). To accomplish this task, each group member was asked to suggest a method by which to measure each variable (with units), and to define ecologically meaningful discrete states within the range of measurements. Members were asked to provide documentation to support their choices. Facilitators analyzed the responses provided and used the suggestions to create mutually exclusive states (with units) for each variable. In February 2008, after slight modifications by the working group, the mutually exclusive states (see Fig. 3) and the units upon which they were defined were ratified (see Supplemental Appendix 2, available online at https://www.eaglehill.us/SENAonline/ suppl-files/s12-Sp4-1040f-McAbee-s2, and, for BioOne subscribers, at http:// dx.doi.org/10.1656/S1040f.s2). Using these unique states for each variable, initial BBN structure was parameterized with conditional probabilities. The conditional probability is the probability that a given state would be achieved, given the states of the contributing variables. For example, the probability that non-native predators are present is a function of the presence of Castor canadensis Kuhl (Beaver) (Fig. 3), K.T. McAbee, N.P. Nibbelink, T.D. Johnson, and H.T. Mattingly 2013 Southeastern Naturalist 148 Vol. 12, Special Issue 4 because Beaver create habitat suitable for non-native predators. Group members were asked to work independently to assign conditional probabilities for all variables and discrete states. Individual member responses were averaged to parameterize probabilities for variables within the model. All members then accepted the conditional probabilities, which resulted in the alpha model (sensu Marcot et al. 2006b) of the Blackside Dace BBN shown in Figure 3. Model context and objectives Temporally, our BBN represents the influence of current conditions on Blackside Dace populations over a 5-y period. Ecological conditions (including a population estimate) are measured at year 0, while the population response is predicted for year 5. The working group decided to measure population response on a 5-y time step because the lifespan of Blackside Dace is estimated to be 3–4 y (Etnier and Starnes 2001, USFWS 1988, Supplemental Appendix 1). By using a time period slightly longer than the lifespan, users can predict local extirpation if no reproduction or colonization occurs. The spatial extent of the model was 200-m stream reaches known to harbor Blackside Dace populations. This extent was chosen to duplicate the population- Figure 2. Influence diagram demonstrating the cascade of ecological influences on population response of Blackside Dace, Chrosomus cumberlandensis. The overall structure demonstrates that human and environmental inputs (level 1) influence ecological variables (levels 2a and 2b) and life history (level 3) that shape local Blackside Dace population response (level 4). 149 K.T. McAbee, N.P. Nibbelink, T.D. Johnson, and H.T. Mattingly 2013 Southeastern Naturalist Vol. 12, Special Issue 4 density and habitat-modeling studies conducted by Black et al. (2013a, b [this issue]). Data for model variables describing fish densities and stream habitat conditions were considered in the context of 200-m stream reaches. However, human inputs and associated upland habitat variables were based on conditions in the entire watershed upstream from these sites. We recognized that only one 200-m reach should be established within a 12-digit hydrologic unit boundary (HUB) (US Geological Survey 2007). The fundamental objective of the working group was to maintain Blackside Dace populations in currently inhabited streams. Therefore, the model outcome was designed to predict population trends at currently monitored sites. The population response was represented by four states: local extirpation, population decline, stable population, and population growth (Fig. 3). Population growth and decline were defined as a population change >10% in the positive or the negative direction, respectively. A stable population was defined as one with a change of less than 10%. Model outcomes were broken into states that represent absolute failure (extirpation), biological take (population decline), and success (stable and growing populations). These outcomes may be interpreted differently based on the state of the initial population, but nonetheless they provided a way to characterize population response to inputs. Figure 3. Division of influence diagram variables into mutually exclusive states, constituting the initial BBN structure for Blackside Dace, Chrosomus cumberlandensis. Gray boxes indicate model components that are not influenced by other model components. K.T. McAbee, N.P. Nibbelink, T.D. Johnson, and H.T. Mattingly 2013 Southeastern Naturalist 150 Vol. 12, Special Issue 4 It is important to note that we did not consider specific populations during model creation and analysis, but rather a general or example population. Therefore, the responses were not conditional to existing population sizes, but were simply bounds for expected responses to guide the working group. When working group members were developing the model, they simply needed to know how population growth and decline were defined. If the model was to be used for specific populations, a “current population size” input would li kely be needed. The means objectives of the project included maintaining quality habitat for the species. Stream habitat conditions have been the most heavily investigated aspect of Blackside Dace ecology, and therefore, were the key proximal influences within the BBN (Fig. 2: level 2b). The habitat modeling study by Black et al. (2013b [this issue]) demonstrated that Blackside Dace presence/absence during summer is strongly associated with conductivity, with water temperature also identified as an important contributing variable. Furthermore, Blackside Dace reproductive activity is negatively related to substrate embeddedness (Mattingly and Black 2013 [this issue]). Therefore, conductivity, water temperature, and substrate embeddedness were central to the model. Each of these variables was also divided into unique states based on the results of their associated studies (Supplemental Appendix 2). For example, habitat models show that Blackside Dace are much more likely to inhabit streams with conductivity below 240 μS (Black et al. 2013b [this issue]). The working group also chose to include a measure of invertebrate community health to represent the Blackside Dace forage base, and to act as a surrogate for other water quality parameters (Barbour et al. 1999). Land-use inputs, represented by nodes at the top of the network (Fig. 2: level 1), are those conditions most likely to occur in the region and subsequently affect the ecology of the Blackside Dace, specifically the three local habitat variables described above. Information provided in Supplemental Appendix 1 suggests that mining and forestry practices pose the greatest threat to the persistence of Blackside Dace. Secondary threats throughout the species’ range include human development (e.g., urbanization, construction), poor livestock practices, road crossings that restrict fish movement, and sedimentation of streams from unpaved roads (Supplemental Appendix 1). The presence of Beavers was selected as a land-use input because Beavers alter stream conditions in ways believed to degrade Blackside Dace habitat (Compton et al. 2013 [this issue]). Streamflow was selected as an environmental input because water levels and flow regime are key aspects of fish habitat (e.g., Freeman and Marcinek 2006). Ecological model components (Fig. 2: level 2a) included upland habitat variables linking land-use inputs to stream-habitat conditions and biological effects. For example, forestry practices can increase substrate embeddedness through sedimentation inputs, but riparian buffers can mitigate this effect (Barbour et al. 1999). The watershed completeness variable represented percent forest cover outside the riparian zone, but within the watershed. Two additional variables, non-native predators and physical barriers to movement, were included as binary states of present or absent. 151 K.T. McAbee, N.P. Nibbelink, T.D. Johnson, and H.T. Mattingly 2013 Southeastern Naturalist Vol. 12, Special Issue 4 Human and environmental inputs and associated upland conditions influence stream habitat conditions (conductivity, stream temperature, substrate embeddedness, and the invertebrate community, Fig. 2: level 2b). These variables subsequently influence Blackside Dace individuals at different stages of life history. Therefore, three model components described the success of juveniles, adults, and immigrants (Fig. 2: level 3). Finally, the three life-history population parameters shape the management outcome of interest, Blackside Dace population response (Fig. 2: level 4). Sensitivity analysis As part of model refinement, sensitivity analysis was performed using Netica for the completed Blackside Dace BBN to identify variables having the greatest influence on the outcome of interest (Clemen 1996, Rieman et al. 2001). BBN sensitivity analysis determines the relative influence of an individual variable by varying it across all possible states, while keeping all other variables constant (Rieman et al. 2001). Resulting variation in the outcome probability can therefore be attributed to changes in the variable being tested. Specifically, we calculated the relative influence of network components on the population response outcome. Although the outcome contained four states, for ease of communication we reviewed only the sensitivity of the “local extirpation” state because minimizing the probability of local extirpation was a fundamental objective of interest. Results of the sensitivity analysis were presented to the working group before the model was ratified to assess whether parameterization of model components met the expectations of experts (Marcot et al. 2006b). Scenario building Changing one or more aspects of the model to test “what if ” scenarios is especially important when the influencing variables in the model contain uncertainty (Clemen 1996). In our model, uncertainty was high because a substantial portion of the model was parameterized using expert opinion rather than data from empirical studies. Using this type of analysis, users may want to refine the model, carefully consider variables of importance when making decisions, or design monitoring strategies to reduce key uncertainties. In our scenario analysis, we focused on creating combinations of inputs that represented real-world management conditions for the Blackside Dace by setting input variables to specific states, but allowing intermediate variable states to vary based on probability tables. Input variables set to known states for scenario building were mining impacts, livestock practices, forestry practices, development, unpaved roads, presence of Beavers, road crossings that restrict fish passage, streamflow, and size of nearby and distant Dace populations. Four scenarios were developed to explore effects of alternative input conditions on model outcomes: (1) optimal input states (described below), (2) single environmental stressors (drought and Beaver presence), (3) land-use change caused by humans (logging and mining), and (4) multiple stressors. Optimal K.T. McAbee, N.P. Nibbelink, T.D. Johnson, and H.T. Mattingly 2013 Southeastern Naturalist 152 Vol. 12, Special Issue 4 input states (scenario 1) included all human land-use variables (mining, logging, livestock, human development, and unpaved roads) set to “none”, and the road crossing variable set to “best management practices” (for detailed description of BMPs see Supplemental Appendix 2). Optimal input states also included environmental inputs being set to represent Beaver absence, strong metapopulation interaction (large Blackside Dace populations both nearby and distant), and above-normal stream flow. We tested the effect of single environmental stressors (scenario 2) by changing the state of a single input variable to a less-than-optimal state, but maintaining all other inputs at the optimal state. This test demonstrated how the associated component influenced Blackside Dace population response when all other conditions were optimal. For example, changing the stream-flow state to “below normal” represented drought conditions. We assessed the response of the model to human land-use components by changing the prior probabilities of certain model components to known states (scenario 3). For example, when the forestry practices node was set to “BMPs” and all other inputs were at optimal values, the model represented a pristine watershed being logged. Finally, given that optimal conditions are uncommon under field conditions and watersheds are often exposed to multiple stressors acting together, in scenario 4 we tested the response of our population outcome by varying the prior probabilities of environmental (drought and Beaver) and/or anthropogenic (logging and livestock) model components. Results Sensitivity analysis The probability of local extirpation of Blackside Dace was strongly influenced by the density of adults and juveniles (Fig. 4), an expected result because, logically, future population status would be sensitive to current population size. Among other model parameters, extirpation was most strongly influenced by conductivity in the stream habitat-condition level, and mining practices in the human land-use level of the BBN. Varying stream conductivity across the three possible states caused the probability of local extirpation to vary between 26% and 74%, illustrating that changes in conductivity greatly altered the predicted Blackside Dace population response. The direct connection between mining practices and elevated conductivity was a product of the working group’s parameterization of the BBN, and likely stemmed from their awareness of the recent study by Black et al. (2013b [this issue]), and from field experience and professional judgment. The likelihood of extirpation was moderately sensitive to a number of other variables such as invertebrate density, substrate embeddedness, and riparian buffer quality (Fig. 4). Extirpation probability showed almost no sensitivity to eight variables, including the size of nearby or distant Blackside Dace populations, unpaved roads, and road crossings (Fig. 4). 153 K.T. McAbee, N.P. Nibbelink, T.D. Johnson, and H.T. Mattingly 2013 Southeastern Naturalist Vol. 12, Special Issue 4 Scenario-building results Optimal conditions. Under optimal conditions (defined above), a Blackside Dace population sampled in a 200-m reach within a 12-digit HUB was predicted by the BBN to have a 10.3% probability of local extirpation and 9.3% probability of declining after 5 y. In addition, the population was predicted to have a 45.7% and 34.7% probability of remaining stable or growing, respectively (Table 1). By summing the probabilities of the two undesirable states (extirpation and decline), the model yielded a <20% chance of a population being negatively impacted under optimal habitat conditions. The group agreed that a 10% probability of local extirpation in 5 y seemed higher than expected, but chose to not to make changes to the model parameterization because they believed the model represented the current state of knowledge. Single environmental stressors (drought and Beaver). The presence of drought increased local extirpation (12.6%) and declining population (10.9%) probabilities Figure 4. Sensitivity of the mean probability of local extirpation state at the population response node for Blackside Dace, Chrosomus cumberlandensis. The probability of local extirpation is most influenced by adult density, stream conductivity in the stream habitat condition level (level 2b) and mining practices in the input level (level 1). The bars show the change in local extirpation probability when the associated node (y-axis) is changed across their possible states, while all other nodes remain constant. Human land-use inputs are in dark gray, and biological inputs are in light gray. K.T. McAbee, N.P. Nibbelink, T.D. Johnson, and H.T. Mattingly 2013 Southeastern Naturalist 154 Vol. 12, Special Issue 4 only slightly from the optimal scenario, indicating that Blackside Dace populations should be minimally affected by drought conditions over monthly time periods when other inputs are optimal (Table 1). Similarly, the presence of Beavers only slightly increased the probability of local extirpation (13.9%) and population decline (13.1%) from optimal conditions (Table 1). This prediction was influenced by the model time extent (5 y). Beavers are thought to have a legacy effect such that they may not greatly influence habitat structure until years after colonization. A model representing a longer time step (10–15 y) would likely indicate a stronger effect of Beaver presence on Blackside Dace extirpation. Changing land use. The predicted Blackside Dace population response to a logging operation using BMPs was only slightly more negative (11.5% extirpation and 10.3% decline probabilities) than optimal conditions (Table 1). In contrast, mining in the watershed showed a much stronger effect. When the mining practices node was set to “low”, extirpation had the highest predicted probability (33.8%) of the four population response states in that scenario (Table 1). Moreover, with mining being set to “high impact”, the probability of extirpation (51.6%) was higher than the three other population response states combined for that scenario (Table 1). These results followed the expectations of the group (and results of the sensitivity analysis) indicating that of all the input nodes, mining has the greatest potential to influence Blackside Dace population response. Multiple stressors. Combinations of the less-influential nodes from sensitivity analysis (see Fig. 4) such as drought, Beaver activity, and logging with BMPs, did not predict less desirable outcomes for Blackside Dace populations compared to the mining influence discussed above (Table 2). In contrast, if drought were to occur in a watershed containing low- or high-impact mining, Blackside Dace populations were predicted to show negative trends with higher probability than positive trends (Table 3). In fact, combining mining with just one other Table 1. Predicted population response under optimal conditions and when changing the state of a single node. Under optimal conditions, Blackside Dace have a relatively low probability of negative population trends. Unless described, all other input nodes are set to levels optimal for Blackside Dace. Mining showed the greatest potential for population effects, tripling the probability of extirpation, even for low-impact mining, whereas other disturbances (e.g., drought, Beaver, logging) increased the probability of local extirpation only slightly. Predicted population response (% probability of being in each state) Local Population Stable Population Inputs extirpation decline population growth Optimal 10.3 9.3 45.7 34.7 Drought (below normal streamflow) 12.6 10.9 44.9 31.6 Beaver present 13.9 13.1 44.4 28.6 Logging with BMPs 11.5 10.3 45.1 33.1 Low-impact coal mining 33.8 14.9 31.7 19.6 High-impact coal mining 51.6 16.2 21.2 11.1 155 K.T. McAbee, N.P. Nibbelink, T.D. Johnson, and H.T. Mattingly 2013 Southeastern Naturalist Vol. 12, Special Issue 4 sub-optimal node state predicts a more than one-third probability that Blackside Dace will become locally extinct over 5 y in all instances (Table 3). The influence of mining on the population response was most strikingly demonstrated by the prediction that even low-impact mining (with all other nodes being set to optimal) would have a more negative influence on Blackside dace populations than if mining were absent and all other input nodes were set to their least favorable states (Table 4). Table 3. Predicted population response under multiple stressors that include mining. Highly negative population response trends are predicted when less influential inputs occur in combination with mining. Unless described, all other input nodes are set to levels optimal for Blackside Dace. Predicted population response Inputs (% probability of being in each state) Local Population Stable Population Initial stressor Mining level extirpation decline population growth Drought Low impact 37.9 14.8 29.6 17.6 High impact 57.1 14.9 18.6 9.4 Logging with BMPs Low impact 34.5 15.1 31.3 19.0 High impact 52.7 16.2 20.6 10.5 Beavers present Low impact 38.6 15.9 29.0 16.4 High impact 56.9 15.8 18.2 9.1 Table 2. Predicted population response under multiple stressors. Highly negative population responses are not predicted for combinations of less influential inputs. Unless described, all other input nodes are set to levels optimal for Blackside Dace. Predicted population response (% probability of being in each state) Local Population Stable Population Stressors extirpation decline population growth Drought and Beaver presence 16.3 14.4 42.9 26.4 Drought and logging without BMPs 17.8 14.4 41.7 26.1 Logging without BMPs and livestock 16.3 13.7 42.6 27.4 accessing the stream Table 4. Comparison of mining influence to the cumulative influence of all other input nodes. The probability of negative population responses are more likely when mining occurs alone than when all other stressors act cumulatively. Inputs Predicted population response (% probability of being in each state) Other input Local Population Stable Population Mining nodes extirpation decline population growth None Least optimal 25.3 18.2 36.8 19.7 Low impact Most optimal 33.8 14.9 31.7 19.6 High impact Most optimal 51.6 16.2 21.2 11.1 K.T. McAbee, N.P. Nibbelink, T.D. Johnson, and H.T. Mattingly 2013 Southeastern Naturalist 156 Vol. 12, Special Issue 4 Discussion The objective of this project was to improve recovery management of Blackside Dace by creating a comprehensive, integrated model based on current ecological knowledge of the species. We accomplished the objective by constructing a BBN and providing a quantitative framework that can describe potential changes to the system, both human and environmental. Because the model is quantitative, managers can compare predictions with real-world outcomes and update the model based on new information. Our model allows users to assess the expert belief upon which the model is based, refine model structure to better represent actual outcomes, and update ecological knowledge. The model is also transferable because it was intentionally comprehensive, incorporating conditions throughout the species’ range. Managers can therefore apply the model to any Blackside Dace population across the range of the species, and in a range of environmental conditions. Most importantly, the model is transparent. By explicitly describing an influence diagram for Blackside Dace (and par ameterizing the diagram in a BBN framework) important ecological relationships have been effectively defined and communicated, thereby facilitating communication among stakeholders and peer review of the model. Because our model incorporated ecological complexity, multiple sources of uncertainty, and differing stakeholder values, further development of this model through the addition of management decision alternatives would allow it to be used in a formal structured decision-making (SDM) context (Clemen 1996). BBNs are well suited for incorporation into a SDM framework because they are conceptual models that can convert to a decision-support model (Peterson et al. 2013). We believe adding key recovery elements for the species would provide the necessary components of a SDM framework. Outcomes important to the ESA recovery planning process (i.e., recovery goals) could serve as the decision outcome; and action alternatives that management agencies may undertake could serve as the decision inputs. Decision-making for species recovery under the ESA presents unique management challenges (Marcot and Molina 2007). Imperiled species are often highly specialized, narrowly dispersed, newly classified, or very sensitive, making them difficult to detect in the field or to use in experimental studies (Flather and Sieg 2007). These obstacles are associated with data deficiencies and high uncertainty about system dynamics (Marcot and Molina 2007). As a result, managers often rely initially on expert judgment when creating recovery plans. Because these judgments can become ingrained as fact rather than be treated as valuable yet untested hypotheses, it is imperative to re-evaluate recovery goals over time. To this end, we suggest that recovery management for many listed species would benefit from an integrated modeling approach like our BBN, ultimately incorporated into SDM approach appropriate for adaptive resource management decision-making. Limitations and future work Before an alpha-level model is used to implement management actions, it is recommended that the model undergo a formal peer review process to ensure 157 K.T. McAbee, N.P. Nibbelink, T.D. Johnson, and H.T. Mattingly 2013 Southeastern Naturalist Vol. 12, Special Issue 4 credibility and effectiveness (Marcot et al. 2006b). Because peer review and refinement are important aspects of the modeling process, the model should be sent to both BBN and Blackside Dace experts in order to receive independent agreement that the model is mathematically valid and contains appropriate ecological relationships. One structural component that is important to assess is the differing hypotheses that were combined in order to reach model consensus (see Development of initial BBN section). By striving for consensus, we may have masked structural uncertainty of competing hypotheses. Assessing the variation or range in elicited probabilities is a key sensitivity analysis that can test this structural uncertainty. We acknowledge that structural uncertainty arising from alternative structures can be important to assess (Regan et al. 2002), but at this stage, the group felt that consensus was paramount. Therefore, testing of structural uncertainty was tabled for future iterations. The strong influence of conductivity on Blackside Dace persistence is supported by habitat models (Black et al. 2013b [this issue]), but these authors suggest that conductivity may only be an indicator or surrogate for another detrimental phenomenon. For example, heavy metals are known to be associated with acid mine drainage (Gray 1997, Petty and Barker 2004), and have both lethal and sublethal effects on fish (e.g., Henry et al. 1999), but this relationship has not been investigated for Blackside Dace. In addition, to our knowledge, a comprehensive analysis of natural variations in the conductivity of streams in the Upper Cumberland River drainage has not been completed. Because conductivity is one of the most influential variables in our model, we advise that increased research emphasis be placed on developing a mechanistic understanding of the relationship between conductivity and Blackside Dace persistence. To more effectively describe how the environmental variables are affecting Blackside Dace, our model can be refined by improvements to environmental input states. For example, Beavers are described in the model as being either “present” or “absent”. However, this does not effectively convey the legacy effect that Beaver colonization may have on stream habitat structure and Blackside Dace population response (Compton et al. 2013 [this issue]). Beaver can alter aquatic habitat by increasing channel width, opening riparian canopy, and altering flow regimes (Naiman et al. 1988). Alterations by Beaver have temporal effects on aquatic habitat, such that stream habitat may differ one, five, or fifteen years after Beaver have colonized the area (Naiman et al. 1988). Rather than classify Beaver as present or absent, the model might be improved by setting the states of Beaver activity based on the number of years since Beaver colonized a stream, with zero indicating Beaver absence. For our BBN, however, the working group members did not approve any assumptions about the effects of Beaver beyond simple presence/absence. Changes to the definition of average streamflow would offer similar improvements. Rather than considering streamflow at a single point in time, it would be more ecologically sound to describe the compounding effects of multiple years of K.T. McAbee, N.P. Nibbelink, T.D. Johnson, and H.T. Mattingly 2013 Southeastern Naturalist 158 Vol. 12, Special Issue 4 drought. Consecutive years of drought have different impacts on fish communities than seasonal droughts (Lake 2003), and may prompt Blackside Dace to alter habitat usage as well. Potential changes to the model highlight the ability for peer review and criticism of the model to be quickly adopted. Revising the recovery plan for Blackside Dace in which new goals are quantified by this model is certainly possible. New recovery goals could be defined based on the probability of populations becoming locally extirpated. For example, one such goal that builds on the current recovery plan might read, “at least 3 streams in each of 8 sub-basins must always have <15% probability of local extirpation over the next 20 years.” In this instance, a new model based on a 20-year time period would need to be constructed. Conclusions In the recovery plan for Blackside Dace (USFWS 1988), as with many recovery plans, the multiple aspects affecting species recovery are only weakly or conceptually connected, and uncertainties are not addressed in any formal fashion. In contrast, the BBN presented here provides a framework to quantitatively link these aspects together in a flexible model to assist Blackside Dace recovery management in the present, and it can be updated through time to reflect new scientific knowledge. Potential conflicts between human land use and Blackside Dace can now be evaluated with a contemporary understanding of the system. It is noteworthy that stream conductivity and mining practices emerged from our analysis as major drivers of Blackside Dace population sensitivity, which suggests the need for additional research to confirm expert opinion and identify causal mechanisms. In summary, our modeling framework enhances the capacity for recovery management today while opening the door to an informed Blackside Dace management system for the future. Acknowledgments Funding was provided by a Challenge Cost Share agreement between the National Park Service and the University of Georgia, administered through the Piedmont-South Atlantic Cooperative Ecosystem Studies Unit as Task Agreement J5028000705. K.T. McAbee was supported by funding from the University of Georgia Graduate School and the Warnell School of Forestry and Natural Resources. Additional support was provided by US Fish and Wildlife Service (USFWS) HCP Planning Assistance grants to TWRA administered through The Nature Conservancy to Tennessee Technological University (TTU). Completion of the manuscript was facilitated by a 2011-2012 TTU Faculty Non-Instructional Assignment to HTM. Amy Knox constructed Figure 1. We especially thank Blackside Dace species experts who completed survey questionnaires and served on the working group. Special appreciation is extended to Michael Floyd, USFWS Kentucky Field Office, for coordinating field visits, and to the Big South Fork National River and Recreation Area for providing field housing. Earlier drafts of the manuscript were improved by comments from Shannon Albeke, C. Rhett Jackson, James Long, James Peterson, Angela Romito, Julie Wilson Stahli, and two anonymous reviewers. 159 K.T. McAbee, N.P. Nibbelink, T.D. Johnson, and H.T. 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