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Suggested Citation:"3 Ecological Modeling." National Academies of Sciences, Engineering, and Medicine. 2017. Review of the Edwards Aquifer Habitat Conservation Plan: Report 2. Washington, DC: The National Academies Press. doi: 10.17226/23685.
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3

Ecological Modeling

One of the major efforts set forth by the Habitat Conservation Plan (HCP) is the creation of ecological models for the Comal and San Marcos systems. The HCP describes the overall goals of the modeling effort: “The EAA will . . . develop a predictive ecological model to evaluate potential adverse ecological effects from Covered Activities and to the extent that such effects are determined to occur, to quantify their magnitude. The model results will help the Applicants develop alternative approaches or possible mitigation strategies, if necessary.” The ecological models should be able (1) “to predict specific ecological responses of the Comal and San Marcos Springs/River ecosystems and associated Covered Species to various environmental factors, both natural and anthropogenic”; (2) “to assist in establishing potential threshold levels for these ecosystems and associated species relative to potential environmental stressors”; and (3) “to assist the overall scientific effort to better understand the interrelationships among the various factors affecting the dynamics of these ecosystems and associated species.” The models are also expected to be able to account for impacts to the ecosystems from both management measures and natural variations, including such things as groundwater withdrawal, recreation activities, parasitism, and restoration actions. The HCP later describes several other structural and operating requirements for the models, but does not go so far as to prescribe exactly which listed species should be included and what processes should be encompassed, noting only that the models should be capable of including plant, animal, hydrological, climatic, and management variables, and simulating interactions among all of these components. In response to the HCP, the Edwards

Suggested Citation:"3 Ecological Modeling." National Academies of Sciences, Engineering, and Medicine. 2017. Review of the Edwards Aquifer Habitat Conservation Plan: Report 2. Washington, DC: The National Academies Press. doi: 10.17226/23685.
×

Aquifer Authority (EAA) created an ecological modeling team consisting of academics, government scientists, consulting firms, EAA employees, and others to develop the first version of the models by December 2016. They focused on the population dynamics of the fountain darter (FD) and the spatial and productivity dynamics of key submersed aquatic vegetation (SAV) species.

The Committee has reviewed the progress made on the ecological modeling twice prior to this report. NRC (2015) discussed the basic design of the FD model, including the decision to develop an individual-based model, and it opined on several precursors to the model, such as the habitat suitability analyses done for FD, Texas wild rice, and the Comal Springs riffle beetle (CSRB). NASEM (2016) reviewed the first complete report from the ecological modeling team on what is now expected to be the sole product—models that predict the abundance of SAV and FD, each run separately and also run in a coupled mode. This chapter has two goals: (1) to address the EAA’s responses to NRC (2015), and (2) to suggest scenarios for the FD model to run, now that a calibrated version is available. NASEM (2016) is provided as Appendix A to this report, and some of the recommendations made in that report are summarized in Box 3-1. It is expected that the reader will be knowledgeable about the contents of NASEM (2016) prior to reading this chapter. The comments and suggestions for scenarios presume that the recommendations in NASEM (2016) have been sufficiently addressed.

EAA RESPONSE TO THE COMMITTEE’S FIRST REPORT

Recommendation for Development of a Conceptual Model

In NRC (2015), the Committee “recommended that as a top priority the EAA develop an ecosystem-based conceptual model, or a series of conceptual models of increasing resolution, that show how water quality and quantity, other biota, and restoration and mitigation activities are expected to interact with the indicator species, as well as with all covered species. Boxes in the conceptual model would represent targets of the monitoring program, while arrows linking the boxes would represent quantitative or empirically derived relationships between the boxes based on research. Such interactions for which too little data are available to establish empirical relationships could be targeted for monitoring and further research during the permit period.” Also, with respect to project integration, NRC (2015) states that “the HCP would benefit from more formal integration to enable clear explanation of the many sets of results emanating from the monitoring, modeling, and research efforts. Without greater attention to project integration, there is a danger that the large number of separate projects will not combine seamlessly into an overall science program.” An overall con-

Suggested Citation:"3 Ecological Modeling." National Academies of Sciences, Engineering, and Medicine. 2017. Review of the Edwards Aquifer Habitat Conservation Plan: Report 2. Washington, DC: The National Academies Press. doi: 10.17226/23685.
×

ceptual model of the system including hydrologic, climate, and biological components was identified as critical to such integration.

The EAA has now provided a scientifically sound foundation on the way to developing a generalized ecosystem-based conceptual model. The process of developing the FD and SAV models, and the associated con-

Suggested Citation:"3 Ecological Modeling." National Academies of Sciences, Engineering, and Medicine. 2017. Review of the Edwards Aquifer Habitat Conservation Plan: Report 2. Washington, DC: The National Academies Press. doi: 10.17226/23685.
×

ceptual diagrams of how the models work, provide an excellent basis for further development of an overall conceptual model. While the conceptual diagram from the models is not as comprehensive as the Committee suggested in the first report, it is a major improvement over the original influence diagrams of the HCP and reflects well the current level of understanding (related to FD and SAV) in the Edwards Aquifer system. As shown in Figure 3-1, the conceptual models show linkages between potential forcing factors (e.g., spring flows and water quality) and important response variables (SAV and FD abundances). Additionally, the EAA has adopted the multidimensional surface water modeling system (MD-SWMS) for modeling surface water dynamics, the stream water quality model QUAL2E for modeling water quality, and generated submodels for SAV (see Figure 3-1) and FD. We encourage EAA to continue with the conceptualization of the overall ecosystem by building on the FD and SAV conceptual models.

It is hoped that the conceptual models produced to date, and their further expansion to the overall ecosystem, will not only serve to guide development of the predictive models, but will provide a powerful integrative communication tool for the overall HCP and better coordinate the diverse expertise found across EAA’s multiple advisory committees and contractors, particularly in cases where differences in opinion, interpretation, and understanding might be prevalent. In addition, the conceptual and predictive ecological models should be used to evaluate the minimization and mitigation (M&M) measures, in terms of both appropriateness and efficacy. For example, the HCP (on pages 4-43 through 4-45) hypothesizes that M&M measures will have important impacts on habitat and population sizes for FDs, CSRB, and Texas wild rice. The conceptual models can help devise priorities for M&M measures, while measured impacts of the M&M measures can be used to fine-tune the predictive models. As described in NASEM (2016), the progression through model development, testing, and usage is iterative. Thus, as Phase 1 of the HCP progresses, it is expected that M&M priorities, as well as the conceptual and predictive models, will continually improve as new data are collected and incorporated.

Recommendations about Habitat Suitability Analyses

NRC (2015) suggested that, given the absence of an ecological model for Texas wild rice, the current habitat suitability analysis (Hardy et al., 2010) should be treated as a hypothesis and tested for robustness throughout the San Marcos River. For example, the M&M activities could be used to test the validity of using water depth and velocity as the only predictive variables for optimal habitat for Texas wild rice. The Recommendations Review Work Group (RRWG) responded that they were working on this

Suggested Citation:"3 Ecological Modeling." National Academies of Sciences, Engineering, and Medicine. 2017. Review of the Edwards Aquifer Habitat Conservation Plan: Report 2. Washington, DC: The National Academies Press. doi: 10.17226/23685.
×
Image
FIGURE 3-1 Generalized frameworks developed by the EAA providing conceptual linkages between potential forcing factors and important response variables for fountain darter and submersed aquatic vegetation abundances.
SOURCE: Adapted from BIO-WEST, 2015.
Suggested Citation:"3 Ecological Modeling." National Academies of Sciences, Engineering, and Medicine. 2017. Review of the Edwards Aquifer Habitat Conservation Plan: Report 2. Washington, DC: The National Academies Press. doi: 10.17226/23685.
×

(“Continual”), suggesting that the continued replanting of Texas wild rice will be conducted with such tests in mind.

Similarly, NRC (2015) recommended that the habitat suitability analyses done for the FD act as a “back-up” to the individual-based modeling and provide additional quasi-independent results to support a weight-of-evidence approach for FD. The RRWG’s response (“Done”) made it clear that they did not see the value of continuing work on the habitat suitability analysis for FD given their focus on developing the mechanistic ecological model. Nonetheless, they acknowledged that “if the fountain darter module fails or does not calibrate, then suitability should be revisited” (EAA, 2015).

If the suitability analyses are pursued in the future, the EAA should return to NRC (2015) for a thorough evaluation and recommendations on their earlier approach and consider new methods that have evolved to address some of the issues with the classical habitat suitability approach (Guisan et al., 2013; Merow et al., 2014; Hamilton et al., 2015). In particular, such analyses should be based on careful selection of spatial scales, and it is important that spatial and temporal resolution are aligned throughout all sources of data. Furthermore, parameters and the estimation of functional relationships, as well as evaluation of alternative model formulations, should be based on sound statistical metrics.

Recommendations about Comal Springs Riffle Beetle

NRC (2015) stated that prior to being able to include the CSRB in a mechanistic model, it is critical to have a much deeper understanding of the spatial distribution, range of potential habitats, and natural history of the CSRB. This natural history includes understanding the number of generations per year, cohort synchrony or asynchrony, the times of year for reproduction, and the biotic and abiotic variables that influence these dynamics (e.g., siltation). Furthermore, a better understanding of the optimal CSRB habitat is needed to understand how changing flow conditions will impact CSRB. The RRWG responded positively to this item, devoting the 2016 Applied Research budget exclusively to CSRB research projects. There are also planned to be at least two Applied Research projects in 2017 devoted to the CSRB (see Chapter 5).

It is unlikely that an ecological model of the CSRB will be developed in the near future. Regardless, the data being collected on the beetle are potentially of great importance, warranting a few comments about the most recent studies on CSRB abundance in the Comal system. In 2014, as part of the Applied Research Program, the EAA contracted with Zara Environmental for a system-wide estimate of the CSRB population within the Comal Springs ecosystem. As described in detail in Chapter 5, this was the most directed research effort to date for estimating CSRB population

Suggested Citation:"3 Ecological Modeling." National Academies of Sciences, Engineering, and Medicine. 2017. Review of the Edwards Aquifer Habitat Conservation Plan: Report 2. Washington, DC: The National Academies Press. doi: 10.17226/23685.
×

abundance. Nonetheless, there were several serious flaws to the study design, including the sampling approach. Furthermore, results from the final report (Zara Environmental, 2015) suggest that the CSRB populations are very low compared to previous reports that estimated CSRB populations based on the wetted area of potential habitat. The discrepancies between these various population estimates illuminate why a better sampling approach is necessary for estimating the current CSRB population and projecting future changes. Because the long-term biological goals for the CSRB involve both a “qualitative habitat component and a quantitative population measurement” (page 4-9, EARIP, 2012), a better sampling method is also critical to determining compliance with the Incidental Take Permit.

The HCP suggests that the CSRB may be an indicator species for evaluating the impact of covered activities on other listed species. That is, page 4-38 of the HCP states that “In 2010, the EARIP held workshops involving a multi-disciplinary team of biologists to develop influence diagrams regarding the impacts on fountain darters, Texas wild rice, and the Comal Springs riffle beetle. These species were believed to be good indicator species for the impacts on other Covered Species.” There appears to be disagreement about whether the CSRB is an indicator species, particularly because assumptions about CSRB behavior during low flows (retreating into subterranean habitat) may not hold for other invertebrates or amphibians. If the CSRB is abandoned as an indicator species, the EAA should be prepared to develop detailed monitoring plans for the other covered species (e.g., dryopid beetles, Peck’s Cave amphipod, salamanders). Planned Applied Research projects suggest that the EAA is moving in this direction (see Chapter 5).

SCENARIOS FOR ECOLOGICAL MODELING

As part of its statement of task, the Committee was asked to “identify those biological and hydrological questions related to achieving compliance with the HCP’s biological goals and objectives that the ecological and hydrologic models should be used to answer, specifically including which scenarios to run in the models.” EAA has focused the ecological modeling efforts on FD and SAV. Recommendations from the Committee’s June report on the ecological models (Box 3-1, NASEM, 2016) suggest that, given the present state of development of the models, it will be difficult to run scenarios of interest to the EAA. For example, NASEM (2016) states that “the representation of fountain darter growth, mortality, reproduction, and movement may be too simple and not sufficiently linked to changes in habitat and flow to answer some of the important management questions.” “For both fountain darter and SAV, the representation of flow effects in the model is too limited because of reliance on having site-specific empirical

Suggested Citation:"3 Ecological Modeling." National Academies of Sciences, Engineering, and Medicine. 2017. Review of the Edwards Aquifer Habitat Conservation Plan: Report 2. Washington, DC: The National Academies Press. doi: 10.17226/23685.
×

evidence for the effects.” Furthermore, “the historical time period used for calibration of the fountain darter submodel had relatively similar environmental conditions from year to year, which limits the range of conditions of scenarios feasible for exploration by the model.”

Despite these remaining model development and testing steps, this chapter explores the development of scenarios for the ecological models, focusing on the FD model because it is further along in development than the SAV model. Only the most general guidelines for scenarios to run in the SAV model are possible at this time due to the early nature of that modeling effort. The reader is reminded that the issues and recommendations described in the Committee’s previous report (NASEM, 2016, which is Appendix A in this report) should be adequately addressed prior to running the scenarios. Indeed, the manner and degree to which these issues are addressed will determine which scenarios can be run and the confidence level appropriate for interpreting the results of the scenario analyses.

While the following section is largely directed toward the FD model, there is an important relationship between the two modeling efforts. Many of the management actions related to FD will necessarily involve management actions directed at SAV (as FD habitat). Explicit treatment of how actions directed at SAV would affect FD through the coupled models is preferred. However, such explicit analysis requires that the two modeling efforts progress sufficiently to allow them to be coupled. The capability to couple two mature and tested models (FD and SAV) will enable more questions to be addressed (e.g., dynamic and simultaneous responses of FD and SAV to changes in flow) and will make the predictions of SAV effects on FD more defensible.

To varying degrees, the scenarios described below require that the recommendations of the NRC (2016) report (Box 3-1) be adequately addressed. The degree to which the recommendations are addressed will determine the confidence and credibility of the model predictions for many of the scenarios. First, a set of concepts about best practices in designing and interpreting scenarios is described, adapted from Rose et al. (2015); Addison et al. (2013) also offer useful advice on using ecological models for management analyses. The ideas and concepts for designing scenarios apply to both the FD and SAV models, as well to ecological models in general. Then, a set of possible scenarios specific to the FD model are provided to illustrate the types of questions that could be addressed once the model is deemed management-ready. Much discussion about scenario analyses comes from the business community (e.g., Bradfield et al., 2005) and from climate change modeling (Parson et al., 2007; Lempert, 2013), which focuses on possible future conditions.

Suggested Citation:"3 Ecological Modeling." National Academies of Sciences, Engineering, and Medicine. 2017. Review of the Edwards Aquifer Habitat Conservation Plan: Report 2. Washington, DC: The National Academies Press. doi: 10.17226/23685.
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Concepts for Designing Ecological Model Scenarios

Framing of Scenario Analyses

The terms “scenario analysis,” “sensitivity analysis,” “uncertainty analysis,” and “model experiments” are widely used, often interchangeably, in simulation modeling. Similarly, the results of such analyses are called “predictions,” “projections,” and “forecasts.” These words can mean different things to different people, and can create confusion among modelers and end-users leading to miscommunication, improper interpretation of results, and unachievable expectations placed on model products.

Scenario analysis, sensitivity analysis, uncertainty analysis, and model experiments all involve changing model inputs to assess how the model responds. A “scenario” is a coherent, internally consistent and plausible description of a possible future state (http://www.ipcc-data.org/guidelines/pages/definitions.html). Model experiments are where key conditions (which may not be observable in nature) are used in various combinations to perform experiments (e.g., to test hypotheses, experimental design) (Peck, 2004). Sensitivity analysis usually involves the small variation of individual parameters or other inputs, while uncertainty analysis involves realistic and simultaneous variation in model inputs (Saltelli and Annoni, 2010).

Prediction is a general term regarding model results, with a “projection” being considered a less rigorous prediction than a “forecast.” Specific years are typically associated with forecasts, implying the results are what should be expected in nature (e.g., FD abundance in 2021). Projections can be expectations of model predictions over a number of years of calculation, rather than associated with specific dates. Care should be used in whether scenario results are labeled with real years (e.g., 2020, 2021, 2022, etc.) or arbitrary years (10, 11, 12, etc.). The label given to the analyses of management-driven questions using the FD model will be important for clarity and communication.

In addition to properly labeling the model simulations, it is also critical to specify the actual simulations themselves in as much detail as possible to ensure the modeling results will be useful and credible. Often, modeling is considered unsuccessful because of the lack of specification of the questions to be answered, coupled with people having overly high expectations of what the modeling can do (e.g., expect forecasts). To illustrate, a poorly structured question is: (a) What are the effects of low flow on fountain darter? versus the well-stated question (b) How do two consecutive years of 1 percent chance droughts within 20 years of historical conditions of flow affect the long-term (20-year) average annual population abundance of adult fountain darter? Question (a) is vague about the conditions of interest and the response variable and the response variable’s time scale of response.

Suggested Citation:"3 Ecological Modeling." National Academies of Sciences, Engineering, and Medicine. 2017. Review of the Edwards Aquifer Habitat Conservation Plan: Report 2. Washington, DC: The National Academies Press. doi: 10.17226/23685.
×

Question (b) provides critical details on what is meant by “low flow” (two consecutive 1/100 year droughts) and “affect FD” (20-year average adult population size). The questions should also be informed by the types and needs of the management actions being considered under the Habitat Conservation Plan.

The Committee recommends that all scenario questions be well defined and the model results carefully labeled. The more specific the question associated with the scenario is stated, the more likely the model can provide an answer.

Domain of Applicability

Models often have many hidden assumptions. A major hidden assumption is about the range of input values over which certain relationships are valid and the labeling of inputs with general names but then using them in very specific ways. These hidden assumptions, along with the range of conditions over which the model is been evaluated (e.g., calibration and validation datasets), define the domain of applicability of the model. Scenarios that push the model outside its domain result in increasingly uncertain predictions.

To illustrate, consider a fish population model that has an input labeled “flow.” However, the equation in the model that uses flow was a linear relationship of its effect on mortality rate, and estimated over a narrow range of flow values. Also, because of the previously small variation in flow in the years used to calibrate the population model, the effect of flow on other possible processes (e.g., timing of spawning, growth rate) were reasonably ignored. Thus, simply changing flow in the model to represent very low flow years (e.g., drought) can result in inaccurate predictions. Inaccuracies arise because the new values of flow would actually result in more than a linear change in mortality rate than is assumed in the model (inadequate process representation). Also the effects of flow on spawning and growth were ignored because of the narrow range of variation used when the model was formulated (missing effects).

The conditions under which the model was developed should be compared to the conditions for which the model will be used in scenarios, in order to determine the degree to which the model is within in its domain of applicability. Are the changes and expected effects within the range for which the model has been tested or evaluated? Do the effects approach extreme aspects of the relationships where there is high uncertainty or where responses do not adhere to the assumed relationships?

Suggested Citation:"3 Ecological Modeling." National Academies of Sciences, Engineering, and Medicine. 2017. Review of the Edwards Aquifer Habitat Conservation Plan: Report 2. Washington, DC: The National Academies Press. doi: 10.17226/23685.
×

Explicit Versus Implicit Representation

The changes in factors that are varied as part of scenarios can be represented explicitly or implicitly in the model. Explicit representation means that a variable or factor is named in the model description, and its effects within the model appear in equations. An example would be when the growth rates of fish in a model include a relationship that has flow as an explanatory variable (e.g., growth rates peak at some intermediate flow value). Each day the value of flow is used to determine the growth rate of the individual fish for that day. With such a model, no other changes would be needed to test scenarios about how flow affects growth rates and population dynamics. Different time series of flows can be input to the model, and the predicted population dynamics can be compared.

Implicit representations are when the effect of a factor is imbedded within the formulation of the model, and the factor may not appear on any list of variables or parameters or even anywhere in the model equations. The factor is still included in the model, but its effect is built into the relationships without specifying its effect as a model input. In our simple example, flow would not be explicitly part of the growth rate equation, yet the effects of flow on growth rate are included because whatever growth rates were assumed occurred under some set of flows (typically assumed to be average or representative conditions). To examine model responses to changes in flow requires one to simply assume what changes in the growth rate would occur from a changed flow, and then the model can be run with the original and adjusted growth rates. In fact, if done correctly, one would get the same results from the explicit and implicit representations.

Hence, just examining the list of model variables and parameters or oversimplified diagrams of how the model works is not sufficient to judge the realism of what factors can be changed as part of scenarios. Explicit representations should be scrutinized for how the change in any given factor is represented. Implicit representations do not preclude assessing the effects of a factor, but how the changed conditions were realized by altering existing processes or formulations in the model needs to be evaluated. Overly general phrases like “The effect of low flow was . . .” without a clear explanation of what potential effects were included, and not included, should be avoided. The only way to fully understand what effects are included is to examine the model code itself to see the equations and how they are solved, which is not practical in many situations. Careful documentation that goes beyond general word descriptions and box-arrow diagram descriptions of the model is required so that analysts or knowledgeable staffers can easily respond to questions with specific and accurate answers about what actually was affected by the changed flow conditions. It should be noted that the documentation of the FD model has been excellent to date; it should be

Suggested Citation:"3 Ecological Modeling." National Academies of Sciences, Engineering, and Medicine. 2017. Review of the Edwards Aquifer Habitat Conservation Plan: Report 2. Washington, DC: The National Academies Press. doi: 10.17226/23685.
×

noted that all equations, solution methods, and their justification should be documented before they are coded.

Implicit versus explicit representation also applies to spatial and temporal considerations. One does not have to simulate the spatial and temporal scales of every process in order to include their effects in simulations. For example, prey encounters occur on millimeter and second scales, but one does not have to build a model that uses millimeter-sized spatial cells and a one-second time step to include predators’ encountering patchily distributed prey. Finer scales than explicitly represented can be assessed implicitly by generating randomness around the function that relates prey to predator consumption or growth (e.g., Letcher and Rice, 1997).

There should be an explanation of the expected effects of a scenario on, for example, fountain darter abundance, and what and how these effects are represented in the model (either explicitly or implicitly). For each scenario, there should be confirmation that the major effects are represented in a reasonable way. For example, if flow is to be varied, then what processes and life stages are expected to be affected?

Uncertainty, Stochasticity, and Variability

Proper interpretation of the results of a model analysis of alternative scenarios depends on how variability is incorporated into predictions. How does one know whether the predicted FD abundances averaged over 10 years are really different among scenarios? We refer to variability as the combined effects of stochasticity and uncertainty. Examples of stochastic effects relevant to the FD model include the occurrence of drought conditions, variation in spring flows from year to year, and fluctuations in abundances of predators. Common sources of uncertainty are the use of laboratory-based measurements to estimate model parameters, use of multiple field studies that occurred in different time periods, and inability to specify unique formulations of processes in the model because alternative formulations result in equally valid fits to the available data. More measurements reduce uncertainty, but not stochasticity (Ferson and Ginzburg, 1996). Appreciating and keeping track of how variability results from uncertainty and stochasticity sources are important when judging the realism of the model and for determining whether differences among alternative scenarios are biologically meaningful.

Observation (or measurement) error is also important to consider when interpreting the results of scenarios. One’s confidence and ability to detect differences in predictions is based on the validation of the model using data. Treating the data as having no observation error can result in inaccurate determination of model confidence as part of model validation (Stow et al., 2009), and therefore misinterpretation of the ecological significance of dif-

Suggested Citation:"3 Ecological Modeling." National Academies of Sciences, Engineering, and Medicine. 2017. Review of the Edwards Aquifer Habitat Conservation Plan: Report 2. Washington, DC: The National Academies Press. doi: 10.17226/23685.
×

ferences among scenarios. For example, when data are treated as exact or overly precise, the model can be expected to generate differences in order to match the data but, in fact, the differences in the data are not reflective of real differences but actually are indistinguishable due to measurement error. This carries over into scenario analysis by putting too much credibility on differences in model predictions across scenarios, when in fact the validation did not support declaring such differences as ecologically meaningful.

Propagating variability through analyses so that final results are ranges or probability distributions should be considered in most all analyses. Purely deterministic analyses (point or simple trend line predictions without uncertainty) do not capture the true variability observed in nature, and there has been much effort to incorporate stochasticity and uncertainty into fish population and food web models to match natural variation (e.g., Bjørkvoll et al., 2012; Link et al. 2012; Magnusson et al., 2013). However, the details of what sources of uncertainty and stochasticity are being considered in the specified variability of the inputs affect how to interpret the spread of results in the output. Saltelli et al. (2004) note that it is rare that an analysis correctly generates realistic variability that is comparable to the observational data; yet, we often interpret the variability of predictions as what is expected in nature. How the variability in predictions of an analysis was generated should be clearly documented, and its implications on how to interpret results should be fully understood.

Critical questions to ask for each scenario include the following. What sources of stochasticity are represented? Is uncertainty kept track of, including uncertainty in the data used to define the scenarios and from the outputs of other models that are used as input to the fountain darter model? How do the predicted differences between scenarios compare to the expected variability that arises from stochasticity and uncertainty? That is, are the differences ecologically significant?

Relative or Absolute Predictions

Model predictions can be divided into two types based on how their predictions are viewed. Some questions require predictions in native units such as annual FD population abundance, while many other scenarios are better viewed as relative predictions. With relative predictions, model predictions are compared to a simulated baseline condition and results expressed as changes from the simulated baseline. These relative predictions are very useful with long-term simulations (future conditions become unceasingly uncertain) because the assumptions of future conditions are maintained in both the baseline and scenarios simulations, and to compare among alternative management options. Although absolute predictions are very tempting because they directly relate to what happens in nature and

Suggested Citation:"3 Ecological Modeling." National Academies of Sciences, Engineering, and Medicine. 2017. Review of the Edwards Aquifer Habitat Conservation Plan: Report 2. Washington, DC: The National Academies Press. doi: 10.17226/23685.
×

the model output is labeled as an absolute output, we generally have much more confidence in relative (model-to-model) predictions.

To illustrate, using the FD model to predict whether 40 or 50 or 60 CFS is protective requires a rigorous validation process to determine if the model can predict absolute abundances sufficiently well to distinguish among the 40 to 60 CFS conditions. Similarly, determining if the population abundance (number of individuals) will go below some value and interpreting that prediction as what will occur in nature is tenuous. Use of the same model for relative predictions would express the effects of 40, 50, and 60 CFS as the percent change in simulated FD population abundance from a baseline abundance. The expected benefits of different management actions can be compared to each other very effectively using a mix of absolute prediction viewed as semi-quantitative and relative predictions.

An important consideration with relative predictions (and often also with absolute predictions when, for example, different management actions are to be compared to a no-action alternative) is defining what is baseline. Baseline conditions rarely can be simply defined as some pristine condition because such conditions may be poorly known and undocumented (Pauly, 1995; Papworth et al., 2009) or not achievable due to other changes in the system (Balaguer et al., 2014; Duarte et al., 2015). If comparisons are also needed under future conditions, then determining the baseline becomes even more challenging because the historical or present-day baseline must then be extrapolated to what it would be under future conditions (Higgs et al. 2014).

As part of specifying each scenario, the baseline conditions and dimensions of the predictions (temporal and spatial scales; absolute or relative terms) should be clearly stated.

Explanations for Predicted Results

The power of using ecological models is that, not only can state variable or aggregate predictions (such as population abundance) be made, but the modeling can provide the reasons for the predicted responses. All model results can be explained at the level of the processes represented in the model. If an ecological model, such as the FD model, is well constructed and tested, providing the explanations for predicted responses to scenarios beyond just abundance (e.g., changes in stage survival, fecundity, and spatial distributions) can inform management actions.

All predictions for scenarios should include, at some level, model-based explanations of why the predicted response occurred. For the FD model, this would focus on how growth, mortality, reproduction, and movement differed between baseline and scenario.

Suggested Citation:"3 Ecological Modeling." National Academies of Sciences, Engineering, and Medicine. 2017. Review of the Edwards Aquifer Habitat Conservation Plan: Report 2. Washington, DC: The National Academies Press. doi: 10.17226/23685.
×

Iterative Process

Scenario analysis should be used as part of a broader iterative process inherent in all ecological modeling. The perception that ecological modeling is a linear process (developmentà calibrationà validationà scenarios) diminishes the usefulness of the modeling. The iterative aspects during model conceptualization, development, and testing may not be obvious to outside observers but they occur. Scenarios should be defined based on the management needs, to advance our understanding, and to identify critical data gaps. Often, some scenarios result in the model generating counter-intuitive or unrealistic results. The model may have been pushed beyond its domain of applicability or have assumed relationships for processes that no longer apply, or may have missing processes that only become important under the new scenario-defined conditions. This is a positive result because, once resolved, it strengthens the model structure or helps define what conditions the model can be used to examine for future simulation analyses. Understanding the model’s explanation for results can also lead to targeted laboratory and field data collection to improve model formulations and reduce prediction uncertainty (as mentioned in NASEM, 2016, Comment 2).

Example Fountain Darter Model Scenarios

Some example sets of simulations are provided to illustrate the types of questions the FD model could be used to address. These overlap to some degree, and others can be constructed. As mentioned earlier, proper analysis of these scenarios is predicated on the recommendations of NASEM (2016) being addressed. Furthermore, some of these scenarios could be applied to the SAV model once it is further along in development. Finally, some of the scenarios have analogues in Chapter 2, which discusses scenarios for the hydrologic modeling. This is because the two modeling efforts overlap to some extent in their purposes (i.e., both examine aspects of the HCP) and because designing and interpreting scenarios have some commonalities across simulation modeling in general.

Test the Model against Observed Flows

A straightforward scenario would be to use historical flows outside of the calibration and validation time periods to assess FD responses under a wider range of previously observed historical flow conditions (similar to the tests of the hydrologic model mentioned in Chapter 2). This would broaden the domain of applicability of the model.

Suggested Citation:"3 Ecological Modeling." National Academies of Sciences, Engineering, and Medicine. 2017. Review of the Edwards Aquifer Habitat Conservation Plan: Report 2. Washington, DC: The National Academies Press. doi: 10.17226/23685.
×

The Bottom-Up Package of Flow Protection Measures

The effects of the EAA’s so-called “bottom-up package” of flow protection measures could be imposed in the model and compared to FD population dynamics without the package (again, similar to what is suggested in Chapter 2 for the hydrologic model). Assumptions about future conditions could then be imposed on both the baseline (future without projects) and bottom-up package scenarios to help guide management actions.

Systematically Vary Flows

The historical record of flows is a limited subset of possible flow patterns that can vary daily, seasonally, and interannually. These levels of variability are overlain on each other to create many possible patterns of flows in 10- or 20-year time periods. Scenarios that systematically vary the daily and seasonal dynamics (when, duration, magnitude), as well as interannual patterns (e.g., occurrences of droughts), would provide a basis for determining how key characteristics of flow affect processes, life stages, and population abundance of FD. A specific set of scenarios could be designed to determine what conditions of low flows lead to high risk for FD. For example, simulations can be run that vary the frequency of occurrence and timing during the year of 5 and 10 days of low flows, with and without delayed spring flows and in combination with some years being drought years.

Systematically Vary Process Rates

This scenario would involve varying the growth, mortality, reproduction, and movement rates of the individual FD within the model under a suite of flows and other environmental conditions. The idea is to create a map of life stage and process sensitivities, which could also be further defined dependent on flow and spatial region. Each M&M measure can then be viewed as affecting certain life stages and processes, at certain times during the year, and in certain spatial areas. How well the M&M measures match up with sensitive life stages, processes, seasons, and areas can guide monitoring to ensure a high likelihood of detecting local responses to management actions. The results can also be used as part of integration to see how well the portfolio (i.e., the mix of M&M measures and minimum flows) covers important life stages and processes. For example, a portfolio that includes three M&M measures that overlap greatly in affecting reproduction of FD via habitat changes, but without at least one measure affecting growth in a critical life stage, can diminish the likelihood of a population response. This set of scenarios can identify redundancies, weak-

Suggested Citation:"3 Ecological Modeling." National Academies of Sciences, Engineering, and Medicine. 2017. Review of the Edwards Aquifer Habitat Conservation Plan: Report 2. Washington, DC: The National Academies Press. doi: 10.17226/23685.
×

nesses, and gaps in the portfolio of M&M measures and suggest modifications or additions to the measures to increase the probability of redundancy in critical stages and of causing a population response.

Effects of Environmental and Biological Factors

The factors of interest in scenarios do not have to appear in the model to be evaluated. Factors like low dissolved oxygen, sediment removal, algal blooms, gill parasites, and shifts in prey and predator composition can all be examined with the FD model. The M&M measures can be used to determine the magnitude, process, life stage, and location of the likely effects. Most all of the effects of the non-flow-related and perhaps some SAV-related M&M measures would need to be specified outside of the model and then used to change inputs and process representations within the model. For example, if the likely effects of an algal bloom are to reduce food sources, then this can be simulated by reducing growth rates for individuals when they are in the region of the grid where the algal bloom is assumed to occur. The effects of gill parasites could be represented in the model as reduced swimming ability and mortality for larvae and juveniles in historically infected areas. Because some of these changes in factors are done implicitly, the manipulation of the model inputs needs to be done carefully to ensure that the results can be labeled, for example, as the “effects of an algal bloom” and “effects of an M&M measure.” Environmental and biological changes can be done singly and in combinations. Generally, the results of this type of exploratory scenario are best viewed as a screening level to provide a rough idea of whether further, more refined, analyses about that factor are warranted.

Vegetative Habitat

Designing scenarios to explore how vegetative habitat affects FD is difficult at this time. The SAV model that would allow for dynamic and spatially explicit responses of SAV to management actions is not yet operational. Rather, the FD model presently uses observed SAV maps and switches them every six months in simulations to match the historical progression of the observed maps. With the present FD model set-up, one could explore vegetation-related M&M measures and other management actions in several ways. One way is to use an implicit approach and keep the observed habitat maps in simulations but adjust growth, mortality, or reproduction of the FD individuals to reflect when they are in the areas where SAV is expected to respond to the management actions. A second way would be to use the existing maps and manipulate them to reflect expected changes based on the management actions; this is challenging

Suggested Citation:"3 Ecological Modeling." National Academies of Sciences, Engineering, and Medicine. 2017. Review of the Edwards Aquifer Habitat Conservation Plan: Report 2. Washington, DC: The National Academies Press. doi: 10.17226/23685.
×

to implement, which is why the dynamic SAV model is being developed. However, given proposed changes in goals for SAV acreage (BIO-WEST and Watershed Systems Group, 2016), a first effort to evaluate the impact of changed coverage by native versus non-native SAV species on FD populations could represent a useful application of the model for management purposes. A third approach would be to switch the timing of the existing maps within simulations to determine whether simulated FD population dynamics are sensitive to subregional scale and interannual variability in the observed SAV (habitat) record. One could create specific time series of habitat maps that represent six-month periods of “poor” and “good” habitat maps to ask, for example, how do multiple years of “poor” habitat conditions affect the FD population abundance? Similarly, one could use “good habitat” SAV maps in sequence to roughly represent how restoration of SAV would benefit FD. Finally, the habitat maps could be switched in combination with different flow patterns to quantify any interaction effects between habitat maps and flow.

Spatially explicit models are frequently sensitive to the scale at which state variables and forcings are defined. Beyond the suggested scenarios for forcing vegetation maps to garner insights from the FD model, additional simulations can be designed to evaluate the sensitivity of FD to SAV. For example, are there measureable thresholds of SAV acreage in a given reach that result in dramatic increases or declines in FD abundance? If this is the case using forced maps, how might those insights inform the requirements from the SAV model for a coupled modeling framework that is most effective?

Forced Population Reductions and Density Dependence

Simulations in this scenario would force FD population reductions (simply remove individuals on a day in certain areas) and determine the time period that the population remains below a threshold and the subsequent rate of recovery of the population to a healthier value. The fact that the model has very limited density dependence (see NASEM, 2016) constrains the analysis to short-term predictions. Shorter-term predictions are typically more influenced by the state of the population at the time of stress, whereas long-term predictions are influenced by the density-dependence in the model.

General Suggestions Regarding SAV Model Scenarios

As mentioned previously, the SAV model is not yet far enough along in its development for detailed suggestions regarding scenarios. For example, it is not yet clear if sexual or vegetative reproduction will be successfully

Suggested Citation:"3 Ecological Modeling." National Academies of Sciences, Engineering, and Medicine. 2017. Review of the Edwards Aquifer Habitat Conservation Plan: Report 2. Washington, DC: The National Academies Press. doi: 10.17226/23685.
×

represented in the dynamic SAV model, and general information regarding sensitivity analyses that should be used to inform the limits and expectations for model runs are not yet available. However, should the SAV model be successfully launched for these systems, the following general ideas for model applications are offered. As for the FD model, a critical question would appear to be running the model under low flows and for flow protection measures to evaluate the impact on predicted SAV. Further pushing the model to catastrophic scenarios—for example where SAV is only present in refugia—might also reveal some insights regarding recovery following such an event. Clearly, this scenario would require confidence in the model formulations and approaches for simulating reproduction. Although the section above suggested forcing simulated maps of SAV representative of “good” and “bad” years in various virtual time series in the FD model, examining these same questions in a dynamic SAV model would no doubt lead to insights regarding the degree to which the spring and river systems are sensitive to consecutive years of drought. A guiding question behind such a series of simulations might be “how many consecutive years of drought or low flow protection measures can the system withstand?” One of the strengths of the SAV model will be its ability to evaluate M&M measures and help to inform associated adaptive management decisions. Here it would seem valuable to use the model to better understand the degree of long-term maintenance that might be required to eradicate non-native species (how much Hydrilla must be removed before the population comes to a steady state at a small enough coverage to be considered controlled in this system?). Are there lessons from the model that can be used to evaluate the timing of planting or non-native vegetation removal that might serve as testable restoration methods that could help optimize the vegetation removal and planting programs? These scenarios are largely predictive in nature, providing output that can be used to evaluate various protective measures or inform improved restoration. However, the EAA is encouraged to explore the diagnostic abilities of this mechanistic model to better understand the environmental forcings that influence vegetation, and to identify future applied research questions that might best serve management goals.

CONCLUSIONS AND RECOMMENDATIONS

Prior to the release of this report, the Committee provided an evaluation of the progress to date on the ecological modeling efforts of the EAA (see Appendix A). Indeed, that short report (NASEM, 2016) covers progress made through mid-2016, including an evaluation of model objectives and usage, configuration, calibration and testing, and submodel coupling, while much of the above text deals with the EAA’s response to the Committee’s first report (NRC, 2015) evaluating the 2014 year. As stated in

Suggested Citation:"3 Ecological Modeling." National Academies of Sciences, Engineering, and Medicine. 2017. Review of the Edwards Aquifer Habitat Conservation Plan: Report 2. Washington, DC: The National Academies Press. doi: 10.17226/23685.
×

NASEM (2016), the Committee feels that the ecological modeling efforts have made good progress and that scientifically sound frameworks and approaches for the SAV and FD models are in place. For the SAV model, where this report comes in the midst of model development, we send a general message of encouragement. Individual-based models are challenging and complex, in this case several novel solutions are being explored, and continued support of the models will likely lead to very useful products in support of the HCP. The Committee and the EAA model development team both recognize that model development is an iterative process, and so it is expected that the models will continue to reflect new knowledge and understanding with time. The Committee is encouraged by the Applied Research focus on the CSRB for 2016 (see Chapter 5) and looks forward to further assisting the EAA with respect to the Committee’s evaluation of the ecological modeling detailed in NASEM (2016). The following conclusions and recommendations refer exclusively to the material in this chapter.

As requested in NRC (2015), the EAA has now provided a scientifically sound basis for the development of a generalized ecosystem-based conceptual model. The conceptual diagrams produced to date for the FD and SAV ecological models will help to guide further development of whole-system conceptual models. This collection of conceptual models will provide a communication tool for the HCP, will aid in coordination of the diverse expertise found across EAA’s multiple advisory committees and contractors, and will serve an important function, along with the predictive ecological models, to evaluate the appropriateness and efficacy of the M&M measures.

The EAA is making progress on addressing the sampling deficiencies that may limit the ability to estimate the distribution and abundance of CSRB populations. The focus on the CSRB in the 2016 and 2017 Applied Research Program is a substantial effort for addressing the limited knowledge about the distribution and life history features that will be important for understanding how the CSRB responds to environmental variation, including changes in flow and responses during drought conditions. If the CSRB is to remain an indicator taxon for other listed invertebrate and vertebrate species, these gaps in life history and distribution will need to be addressed. Alternatively, the EAA should begin to develop monitoring plans for the other listed species.

The continued development of the FD and SAV models will result in models that can address a wide variety of questions about the effectiveness of flow protection and other M&M measures. The models offer a very powerful tool for combining multiple effects across life stages and space into ecologically relevant end points. Reaping the benefits of the ecological models will likely involve continuing, in some manner, the ecological modeling program beyond the originally anticipated time frame.

Suggested Citation:"3 Ecological Modeling." National Academies of Sciences, Engineering, and Medicine. 2017. Review of the Edwards Aquifer Habitat Conservation Plan: Report 2. Washington, DC: The National Academies Press. doi: 10.17226/23685.
×

Armed with a fully capable FD model, the scenarios analyzed should be designed and documented according to the concepts in this chapter. These include careful designing of the scenarios and use of terminology to ensure transparency, confirming scenarios are within the domain of applicability, associating uncertainty with model predictions, and properly interpreting predictions and providing model-based mechanistic explanations for model responses.

Seven scenarios are described for the fountain darter model, which can be either diagnostic based (e.g., varying process rates) or evaluative (e.g., running the bottom-up package). The scenarios offered demonstrate how the model can be used to examine how extreme flows, process rates, environmental factors, SAV habitat, and episodic population reductions affect FD population dynamics. These results can then be merged with the expected effects of M&M measures to identify the robustness and redundancies of the entire suite of actions.

Only general guidance is given on possible scenarios for the SAV model, as it is not appropriate to provide detailed advice at this stage of model development. Nonetheless, given the recently proposed adaptive management actions related to changing SAV species coverage goals in the HCP, it would be timely to evaluate the longer-term impact of these decisions on the stability of the SAV populations. The prospect of having such a valuable quantitative tool to better understand the effects of M&M measures and predict future states will hopefully motivate those involved to continue developing the SAV model.

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The Edwards Aquifer in south-central Texas is the primary source of water for one of the fastest growing cities in the United States, San Antonio, and it also supplies irrigation water to thousands of farmers and livestock operators. It is also is the source water for several springs and rivers, including the two largest freshwater springs in Texas that form the San Marcos and Comal Rivers. The unique habitat afforded by these spring-fed rivers has led to the development of species that are found in no other locations on Earth. Due to the potential for variations in spring flow caused by both human and natural causes, these species are continuously at risk and have been recognized as endangered under the federal Endangered Species Act(ESA). In an effort to manage the river systems and the aquifer that controls them, the Edwards Aquifer Authority and stakeholders have developed a Habitat Conservation Plan (HCP). The HCP seeks to effectively manage the river-aquifer system to ensure the viability of the ESA-listed species in the face of drought, population growth, and other threats to the aquifer. The National Research Council was asked to assist in this process by reviewing the activities around implementing the HCP.

Review of the Edwards Aquifer Habitat Conservation Plan: Report 2 reviews the progress in implementing the recommendations from the Committee's first report, seeking to clarify and provide additional support for implementation efforts where appropriate. The current report also reviews selected Applied Research projects and minimization and mitigation measures to help ensure their effectiveness in benefiting the listed species.

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