Opinion ID: 3066157
Heading Depth: 2
Heading Rank: 4

Heading: MERITS OF THE BiOp

Text: Before we consider the challenges to the BiOp, we have some preliminary observations. First, the BiOp is a bit of a mess. And not just a little bit of a mess, but, at more than 400 pages, a big bit of a mess. And the FWS knew it. In November 2008, shortly before the FWS submitted its final draft, the California Department of Water Resources commented on a portion of the FWS’s draft. Under the title “The Document is Confusing and Disorganized,” the DWR advised that “the document is especially unrefined. The text is often out of logical order . . . , many actions are fairly vague, and the document is unpolished.” Some portions, DWR wrote, were “largely unintelligible.” The FWS also submitted its draft to a peer review panel, coordinated by an outside engineering firm. One of the questions the FWS asked the panel was whether the BiOp was “organized in a manner that is clear, concise and complete (i.e., is it understandable)?” The panel pulled no punches and responded as follows: “The Panel’s response to this question is ‘no.’ The version of the [Effects Analysis] provided to the Panel was a draft and had not been adequately edited for general organization, consistency across sections in how analyses were described and reported, and for redundancies.” The bottom line was that “most readers would have a difficult time.” SAN LUIS V. JEWELL 51 Both the California DWR and the peer review panel commented on the FWS’s imposing schedule for issuing the BiOp. DWR observed that the “FWS was under an exceptionally difficult deadline to get this document in.” In a comment letter sent from the DWR to Reclamation just two weeks before the deadline, the DWR reflected on the pressures the FWS labored under: “Our concerns with the Draft [BiOp] are extensive but are correctable before the December 15 court ordered [deadline] for its finalization. We are willing to work closely with the FWS to address these concerns quickly. . . .” And the peer review panel complained that its own “review was conducted in a four-day period under a tight schedule.” See Ass’n of Pac. Fisheries v. EPA, 615 F.2d 794, 811 (9th Cir. 1980) (“The Agency itself recognized that its data collection was not as thorough as it otherwise would have been: ‘The time constraints imposed by the statutory deadlines precluded the Agency from conducting an exhaustive sampling program.’”). This challenging deadline was not the fault of the agency, but was set by the same district court that would later hold that the FWS’s rushed BiOp was arbitrary and capricious. See San Luis & Delta-Mendota, 506 F. Supp. at 322 (ordering the FWS to produce a revised BiOp in just nine months—a deadline that would later be extended by three months to give the FWS just one year to produce a new BiOp).15 15 One of the ironies of the district court’s deadlines is that the FWS had less time to produce its opinion than either the district court or we will have had to review it: The 2008 BiOp was issued in December 2008. The district court’s summary judgment decision issued almost two years to the day, in 2010. This opinion issues nearly three years later. The concerns in adhering to deadlines is familiar to courts who must occasionally rush to judgment. Justice Powell once bemoaned the problems arising from deciding cases “late in the Term” when the Court 52 SAN LUIS V. JEWELL We concur in these assessments. The BiOp is a jumble of disjointed facts and analyses. It appears to be the result of exactly what we would imagine happens when an agency is ordered to produce an important opinion on an extremely complicated and technical subject matter covering multiple federal and state agencies and affecting millions of acres of land and tens of millions of people. We expect that the document was patched together from prior documents, assembled quickly by individuals working independent of each other, and not edited for readability, redundancies, and flow. It is a ponderous, chaotic document, overwhelming in size, and without the kinds of signposts and roadmaps that even trained, intelligent readers need in order to follow the agency’s reasoning. We wonder whether anyone was ultimately well-served by the imposition of tight deadlines in a matter of such consequence.16 Deadlines become a substantive constraint on what an agency can reasonably do. In this case, the FWS not only had to write and compile the report—a substantial task in and of itself—but was under pressure to, among other things, produce a reliable population estimate of the delta smelt. Kempthorne, 506 F. Supp. 2d at 373 (faulting the 2005 BiOp for failing to produce such an estimate, and noting that “[t]he viability of Delta smelt has been under scrutiny for over ten years. No party has shown was precluded by its own deadlines from “an opportunity for more thorough consideration of the basic principles at risk.” Robbins v. California, 453 U.S. 420, 435–36 (1981) (Powell, J., concurring in the judgment). Robbins was overruled the following Term in United States v. Ross, 456 U.S. 798, 824 (1982). 16 We recognize that the ESA itself imposes deadlines for a consulting agency to produce a BiOp, but those deadlines may be extended by consent of the requesting agency or the concerned applicant. See 16 U.S.C. § 1536(b)(1), (2). Here, the district court fixed the deadline. SAN LUIS V. JEWELL 53 that producing a reliable population estimate is scientifically unfeasible”). Such scientific tasks may not be as well suited to deadlines as producing written copy; the final product will necessarily reflect the time allotted to the agency. The FWS is currently preparing its third BiOp, again under the orders of the district court and on the district court’s deadlines. Although we ultimately conclude that we can discern the agency’s reasoning and that the FWS’s 2008 BiOp is adequately supported by the record and not arbitrary and capricious, we also recognize that Reclamation has continuing responsibilities under CVP and SWP and that this is likely not the last BiOp that the FWS will issue with respect to the delta smelt, nor is this the last legal challenge that we will hear. Future analyses should be given the time and attention that these serious issues deserve. A. The 2008 BiOp’s Reliance on Raw Salvage Figures to Set the Upper and Lower OMR Flow Limits Was Not Arbitrary and Capricious Under normal pumping operations, enough water is pumped from the OMR that the river’s flow reverses. BiOp at 159–60. As vast quantities of water are pumped from the river, smelt and other fish are entrained in the pumps, where they are captured and counted in fish salvage facilities. Id. Not all smelt are salvaged, however, as juvenile smelt smaller than 20 mm (0.79 inches), smelt still in the larval stage, and some percentage of adult delta smelt, are killed by the pumps. BiOp at 338. The prospects for salvaged smelt are also grim, as smelt rarely survive the salvage process, id.; up to sixty percent of the smelt population is lost each year at the pumping plants, BiOp at 210. Unsurprisingly, the 2008 BiOp found that as the OMR flows became increasingly negative, the entrainment risk and accompanying population loss 54 SAN LUIS V. JEWELL increased accordingly, thereby threatening the smelt population’s continuing viability. BiOp at 163. To mitigate this effect, the BiOp RPA imposed pumping limits expressed in terms of negative OMR flows,17 ranging from !1,250 cubic feet per second (cfs) to !5,000 cfs. BiOp at 280. Which limit is applicable is determined by the location of the smelt population, water turbidity,18 and a range of other factors. BiOp at 280. In determining the OMR flow limits, the FWS relied, in part, on the number of delta smelt salvaged from the fish screening facilities. Although it acknowledged that the number of smelt salvaged only represents “a small percentage of the actual number entrained,” and “is not a good estimate of actual delta smelt mortality through entrainment,” BiOp at 338, the FWS relied on this information because “[d]ata on the salvage of delta smelt is typically used to provide an index of entrainment into the diversion pumps,” BiOp at 145. The OMR flow limit has a great practical significance, not merely to the delta smelt but to Californians, as it represents the ultimate limit on the amount of water available to sustain California’s millions of urban and agricultural users. Appellees challenge the !5,000 cfs upper limit on OMR flows, claiming that the FWS based its calculation of the flow limit solely on information contained in two figures that show 17 Actions 1 and 2 of the RPA protect adult delta smelt migrating upstream in winter months to spawn, and Action 3 protects larval and juvenile delta smelt in the spring after they hatch. BiOp at 280, 282. 18 Water turbidity is the measurement of how much sediment or foreign particles are suspended in the water. SAN LUIS V. JEWELL 55 a significant increase in the number of smelt salvaged at approximately !5,000 cfs: Figures B-13 and B-14. See BiOp at 347, 350. These figures, appellees assert, failed to justify the !5,000 cfs limit because the number of delta smelt salvaged (raw salvage) was not adjusted for the smelt’s estimated population for that year (normalized). Appellees’ objection to the BiOp’s use of raw salvage data, rather than normalized data, in calculating appropriate OMR flow limits is essentially an omitted variable bias argument; appellees assert that the number of smelt salvaged in a year is highly influenced by the total population of smelt that year, and that therefore the BiOp’s failure to account for this relation renders its calculated flow limits unreliable. Put another way, according to appellees, when there are more smelt in the BayDelta, more smelt are salvaged. Any apparent relationship between OMR flows and smelt salvage, therefore, may actually be a relationship between smelt population size and smelt salvage. Failing to account for this will skew, if not invalidate entirely, the analysis. The district court agreed, finding that “[t]he use of raw salvage data, as opposed to salvage data scaled to population size, is problematic because raw salvage figures do not account for the size (or relative size) of the smelt population.” San Luis & Delta-Mendota, 760 F. Supp. 2d at 889. The district court further found that “the use of normalized salvage data rather than gross salvage data is the standard accepted scientific methodology.” Id. It concluded that the FWS’s use of raw salvage data in Figures B-13 and B-14 to determine restrictions on OMR flows was “scientifically unacceptable.” Id. at 891. Because the FWS failed to use the best available scientific data, its !5,000 cfs flow limit in Actions 1, 2 and 3 was arbitrary and capricious. Id.; see id. at 891–94. 56 SAN LUIS V. JEWELL These objections, however, suffer from several problems. First, the FWS appropriately relied on Figures B-13 and B-14 to justify its !5,000 cfs flow limits. In Figures B-13 and B- 14, the FWS acknowledged the uncertainty inherent to modeling the relation between OMR flows and smelt and chose a conservative model, a choice that is within the FWS’s discretion to make. See Nw. Coal. for Alts. to Pesticides v. EPA, 544 F.3d 1043, 1050 (9th Cir. 2008). Second, the !5,000 cfs flow limit prescribed in the RPA was not solely determined from Figures B-13 and B-14, and therefore flaws in those figures do not necessarily doom the BiOp’s conclusions. Third, the BiOp’s OMR flow limits work in tandem with the incidental take statement (ITS), which accounts for population-level impacts. That the FWS could have done more in determining OMR flow limits is uncontroverted. This, however, is not to say that the FWS acted arbitrarily and capriciously; we hold, contrary to the district court, that the FWS’s OMR flow limits are supported by substantial evidence.
calculate flow limits in Figures B-13 and B-14 was supported by substantial evidence Figure B-13 in the BiOp19 is a graph showing the relationship between salvaged adult delta smelt and the OMR flow, measured in cfs. The graph shows a positive correlation between salvaged smelt and the reverse flow of the river. That is, the greater the water pumped through the Jones and Banks pumping stations, the greater the count of smelt 19 Figures B-13 and B-14 are reproduced in the district court’s published opinion. Kempthorne, 506 F. Supp. 2d at 886–87. SAN LUIS V. JEWELL 57 salvaged at those stations. The FWS noted that the graph is upward sloping and linear in the lower half of the curve. At about !5,000 cfs there is a “break” in the data,20 and for flows more negative than !5,000 cfs (meaning more water exported from the Bay-Delta), the upward slope increases at an increasing rate. The FWS sought to verify whether the break in the data was actual. It conducted additional analyses of the data to verify that there was not a natural break at any other point and that any error in the OMR flow rates or salvage could not have caused the break. BiOp at 349–51. The FWS concluded that with “flows more negative than !1683, salvage increased.” BiOp at 351. The FWS stated that “[a] major assumption of this analysis is that as the population of Delta smelt declined, the number of fish at risk of entrainment remained constant.” BiOp at 349. What B-13 did was compare actual salvage numbers with OMR flow. What the FWS did not do—and what the appellees and the district court claim the FWS should have done—was prepare an additional figure in which it compared “normalized” salvage numbers with OMR flows. Normalized salvage would be the measure of the salvaged smelt divided by the total population of smelt,21 effectively 20 The break in the data in B-13 appears to span about !3,000 cfs to just under !6,000 cfs. BiOp at 348. The FWS did not attempt to quantify the break; it simply observed that there “appears to be a ‘break’ in the dataset at approximately !5,000 OMR.” BiOp at 347. 21 Part of the district court’s frustration may be that, to date, no one has been able to produce a reliable population estimate for the delta smelt. In 2007 when the district court sent the 2005 BiOp back to the FWS, it commented that 58 SAN LUIS V. JEWELL yielding figures showing what percentage of smelt each year were salvaged at the pumps. In fact, the FWS itself had stated that it could verify its conclusion “by normalizing the salvage data by the estimated population size based on the [Fall Midwater Trawl] data.” BiOp at 349. The peer review had similarly suggested normalizing the data: The Panel suggests that the use of predicted salvage of adult smelt should be normalized for population size. . . . One way to normalize The viability of Delta smelt has been under scrutiny for over ten years. No party has shown that producing a reliable population estimate is scientifically unfeasible. . . . Without population estimates, it is arbitrary for the agency to conclude that project operations will not result in jeopardy simply because the projects will take relatively fewer smelt than they did in the past, in the fact of the undisputed fact that the smelt population has been declining steadily in recent years. Failing to incorporate any information about smelt population abundance into the setting of the take limits is a fundamental failure rendering the BiOp arbitrary and capricious. Kempthorne, 506 F. Supp. 2d at 373. Despite the court’s warning, the FWS did not conduct a population study, nor did it explain why one could not be conducted. Yet, the FWS, its peer reviewers, and the district court’s experts suggested that there were proxies for population (such as the FMWT count) available, even if a strict population count was not. Sometimes we have to read the shadows to discern the reality behind it: Nearly twenty years after the smelt were declared endangered, we know the smelt population is continuing to decline and is imperiled, but still no one knows how many there are. It must tell us something about the difficulties that inhere in trying to count migrating, two-inch fish. SAN LUIS V. JEWELL 59 salvage for population size is to divide by the previous fall Midwater Trawl (MWT) index. A similar regression model to the one fitted to salvage would relate the normalized salvage to Old and Middle River (OMR) flows. . . . Expressing salvage as a normalized index may help remove some of the confounding of the temporal trends during the baseline period .... As the district court’s experts acknowledged, the FWS faces significant practical challenges in setting OMR flow rates to minimize delta smelt entrainment. For example, day-to-day variations in OMR flows and “noise” in smelt sampling22 used to establish abundance and distribution of the delta smelt are significant confounding factors in determining appropriate OMR flow rates, as is the distribution of the delta 22 “Noise” is a statistical term that refers to the unexplained randomness or variation that is found in a sample. It is of particular concern when statistical samples are small. As Dr. Quinn explained: [W]hat are the uncertainties in the population estimates themselves, and might there be shifting levels of accuracy as population levels change? . . . [It] is certainly true [that population growth rate is an appropriate and reliable measure of the population increases and decreases from year to year], if it is known without error but what are the assumptions about sampling? That is, as smelt become increasingly scarce, does their overall distribution become ‘thinner all around’ or is it ‘patchy’, and how might such changes influence the reliability of data from different surveys of abundance? In general, ‘noisy’ data make it more difficult to detect underlying patterns, even if the patterns are genuine. 60 SAN LUIS V. JEWELL smelt population in relation to the pumps. BiOp at 165, 331, 353–55. A lack of real-time information and variations inherent to environmental systems make precision virtually impossible. BiOp at 165, 331, 353–55. Yet, as even the district court recognized, population numbers of the delta smelt are perilously low, San Luis & Delta-Mendota, 760 F. Supp. 2d at 866, and entrainment by the pumping plants has a “sporadically significant influence on population dynamics,” id. at 877 (emphasis added). In such circumstances, the FWS’s decision to use raw salvage data rather than population-adjusted salvage data reasonably protects the delta smelt population without regard to year-by-year fluctuation in population size. The BiOp notes that this decision was motivated by a concern for the absolute number of smelt entrained in the pumps, not the relative number of smelt: “The current population cannot tolerate direct mortality through adult entrainment at levels approaching even ‘moderate’ take as observed through the historic record of recent decades.” BiOp at 287. Thus, the RPA is designed to “reduce entrainment of pre-spawning adult delta smelt during December to March” and to “[m]inimize the number of larval delta smelt entrained at the facilities” by controlling OMR flow from March to June. BiOp at 280, 357. The analytical approach preferred by appellees and the district court is best gauged to measure the number of smelt entrained at the pumps relative to the population size. This may be a more accurate reflection of the relative impact of OMR flows on the smelt population, but it is not tailored to protect the maximum absolute number of individual smelt, as the BiOp’s approach is; the process of adjusting raw salvage for the smelt population size results in SAN LUIS V. JEWELL 61 normalized numbers, but it does so at the potential cost of minimizing the impact of each individual smelt lost.23 Our deference to agency determinations is at its greatest when that agency is choosing between various scientific models, as the FWS did in the present instance. See Nw. Coal. for Alts. to Pesticides, 544 F.3d at 1050. Facing great measurement uncertainty and a smelt population whose existence is threatened, the FWS chose to be conservative in setting the flow limits in Actions 1, 2, and 3. This choice was well within its discretion; the Supreme Court has held that an agency may choose to “counteract the uncertainties” inherent in its scientific analyses by “overestimat[ing]” known parameters without being unreasonable, Baltimore Gas & Elec. Co., 462 U.S. at 103, and we have upheld an agency’s reliance on models that “yield conservative data because the 23 Appellee-Metropolitan Water District uses “one of the data points located at about !5,000 cfs on Figure B-13” for the year 2000 as an example. An unusually high smelt salvage was observed in 2000, but, as Appellee indicates, the smelt population was also higher than usual in that year. Appellee argues that because “[i]t is to be expected that more fish would be salvaged in a year in which the population was extremely large,” the raw salvage number should be normalized for total smelt population. Accepting appellee’s argument that salvage rates should be normalized, the year 2000 would have actually represented a below average (normalized) salvage. Yet, it is undisputed that an extraordinary number of smelt were salvaged in that year. A normalized analysis of smelt salvage counts the year 2000 as a below-average year, while an analysis of raw smelt salvage counts the year 2000 as an above-average year. The FWS’s choice of analysis influences whether it is the OMR flow’s relative or absolute impact on smelt population that is prioritized. Thus, the quality of the statistical method is not the only relevant factor at play, as the district court erroneously concluded: the BiOp’s choice of one model over the other implicates significantly differing management policies. 62 SAN LUIS V. JEWELL models incorporate the higher of [known potential values] in assessing the overall risk,” Nw. Coal. for Alts to Pesticides, 544 F.3d at 1050. Likewise, we give the FWS great deference in its choice of scientific tools, and, in these circumstances, hold that the FWS did not act arbitrarily or capriciously in choosing an analytical tool that resulted in greater protections for the imperiled smelt population.24 24 The district court relied substantially on the testimony of its experts, Dr. Andre Punt and Dr. Thomas Quinn, in concluding that the BiOp’s reliance on Figures B-13 and B-14 was “scientifically unacceptable.” San Luis & Delta-Mendota, 760 F. Supp. 2d at 891. But these experts present a more nuanced view of the FWS’s use of Figures B-13 and B-14. The results confirm what the FWS had already said: that additional analyses using normalized data from B-13 could have informed the FWS’s conclusions. The experts noted that B-13 was a proper measure, although not the only, and perhaps not the preferable, measure. The experts also testified as to the need for the FWS to set some parameters on OMR flows, and the difficulty in figuring out precisely where the parameters should be. In response to the district court’s question whether it was “unreasonable for FWS to rely in part on the information represented in figure B-13,” Dr. Quinn answered that he did “not regard it as unreasonable for FWS to have relied in part on this figure and the data behind it.” But he cautioned that “[t]o rely entirely on it would, however, have neglected the complexities of the issue . . . . Both the number of fish salvaged and the proportion salvaged . . . are relevant, in my view, as are other kinds of information.” Dr. Punt had a similar reaction: “it was unreasonable . . . to have only relied on the information in Figures B-13 and B-14 rather than on an analysis in which salvage is expressed relative to population size.” (emphasis added). Moreover, although both experts had been critical of the FWS’s failure to run the additional numbers, both were cautious about what the normalized data might have shown, whether any hard conclusions could be drawn from any data set, and even whether normalized data would be preferable to the non-normalized data produced. Dr. Quinn acknowledged that even if the FWS had normalized the data in B-13, “plotting flow SAN LUIS V. JEWELL 63 against a salvage index might not fully capture the risk to the population.” With respect to what B-13 showed, Dr. Quinn cautioned that it was “unwise to overestimate the precision” of the data. He was not convinced that there was a “break” in the data, “though [his] basis for saying so [was] more intuitive than statistical.” He “emphasize[d] that any point value has a measure of arbitrariness. If !5000, then why not !4900 cfs? Given the many sources of variation in the data, it strikes me as necessary to set limits even though there may not be strong statistical basis for a particular figure rather than a slightly different one.” He concluded that although “the validity of [FWS’s] specific flow regimes [was] undermined by the incomplete analyses that were done on the available data,” it was “appropriate for the FWS to have some leeway in making decision and setting limits in their efforts to protect and recover listed species.” Dr. Punt went one step further, recognizing the advantage of non-normalized data: B-13, as it is produced in the BiOp, would be justified if “any entrainment, no matter how small relative to the total population size, has long-term consequences for the population size of delta smelt.” These responses from the experts question the FWS’s failure to expand its analysis and to normalize the data underlying B-13. Had it done so, the experts concluded, the FWS would have had a more complete sense of the relationship between OMR flow and salvage, although it is far from clear that that study would have affected the FWS’s conclusions. What the experts’ testimonies do not support is the district court’s overstated conclusion that the FWS’s “use of raw salvage in the analyses depicted in Figures B-13 and B-14 is scientifically unacceptable” or that “such metrics are meaningless as management tools.” San Luis & DeltaMendota, 760 F. Supp. 2d at 891. Were we only evaluating the experts’ opinion on the BiOp, we would face a difficult question as to the continuing validity of this aspect of the BiOp: it is clear that these two experts believed the BiOp to have fallen short in this analysis, although as Dr. Punt indicates, persuasive justifications exist for the BiOp’s reliance on non-normalized data. It is less clear, however, that the BiOp—even as seen through the eyes of Drs. Punt and Quinn—would be rendered arbitrary and capricious by a sole reliance on Figures B-13 and B-14. We need not reach this question, as we accept the opinions of the district court’s experts only insofar as they are persuasive and informative, and—as will be subsequently described—we independently conclude that 64 SAN LUIS V. JEWELL
influenced by more than Figures B-13 and B-14 The district court’s experts concluded that it was the BiOp’s apparent exclusive reliance on Figures B-13 and B-14 that was problematic: as they acknowledged, the figures could be useful in tandem with other analyses and data. The district court therefore based its invalidation of the !5,000 cfs OMR flow limit on its finding that the limit “depend[s] so heavily” on Figures B-13 and B-14. San Luis & DeltaMendota, 760 F. Supp. 2d at 968. After conducting an independent review of the record, however, we hold that the BiOp’s determination of OMR flow limits was sufficiently influenced by several other population-level analyses in addition to Figures B-13 and B-14. Together with Figures B- 13 and B-14, these analyses provide substantial evidence that supports the BiOp’s !5,000 cfs OMR flow limit. First, the BiOp expressly notes that it employed multiple analyses in determining the OMR flow limits, stating with regards to Actions 1 and 2 that “recent analyses indicate that cumulative adult entrainment and salvage are lower when OMR flows are no more negative than !5,000 cfs in the December through March period.” BiOp at 281 (emphasis added). Although the BiOp fails to specify exactly which studies it relies on,25 at least one of these studies is the BiOp does not exclusively rely on Figures B-13 and B-14 in determining the OMR flow limits. 25 The BiOp should have been more explicit in describing exactly which studies it used in its analysis. Had the BiOp been similarly vague throughout its analysis, its lack of specificity may have presented a problem. Here, however, the BiOp has provided sufficient support for its conclusions. SAN LUIS V. JEWELL 65 contextually apparent: a 2008 study by Wim J. Kimmerer, Losses of Sacramento River Chinook Salmon and Delta Smelt to Entrainment, San Francisco Estuary and Watershed Science (hereinafter “Kimmerer 2008”). Kimmerer’s study was referred to throughout the BiOp, and his data was reproduced in a chart estimating adult entrainment. BiOp at 281. The Kimmerer 2008 study quantified the relationship between population losses and OMR flows between 1995 and 2006, concluding—consistent with Figure B-14—that as OMR flow becomes more negative, delta smelt population losses increase. Moreover, in Figure E-4, the FWS read Kimmerer 2008 to show that when OMR flows are more negative than !5,000 cfs—the “break” observed in Figure B- 13—population loss typically exceeds 10 percent. BiOp at 250 (Figure E-4). Figure E-4 shows a strong correlation between flows more negative than !5,000 cfs and high smelt population losses, supporting Figure B-13’s “break.” See BiOp at 250; see also BiOp at 213 (discussing Figure E-4).26 26 Even if Figure E-4’s correlation were less apparent, not all analyses will yield clear “change points,” and therefore the fact that none is immediately apparent in a study does not mean that the study cannot support an agency’s specifically defined limits; such a holding would effectively prohibit an agency from setting exact limits in any circumstance in which a clear change point did not emerge from the data. In the present situation, Kimmerer 2008 and Figure E-4 present a reasonable justification for a !5,000 cfs limit, which does appear to be an approximate point of some significance. That Kimmerer 2008 may also provide a reasonable justification for a !4,999 or a !5,001 limit does not make the FWS’s policy choice arbitrary and capricious, especially given the significant deference owed to the agency in our review. To do so smacks more of strict scrutiny than arbitrary and capricious review under the APA and ESA. Even were the range of the limit justified by Kimmerer 2008 substantially greater than this, and it most likely is, it is not appropriate for 66 SAN LUIS V. JEWELL Second, the BiOp was more explicit in describing its bases for determining the !5,000 cfs limit for Action 3, stating that “[t]he OMR flows associated with the protectiveness criteria defined above have been derived from particle tracking modeling27 with the input assumptions defined below.” BiOp at 360. Because Action 3 protects larval and juvenile delta smelt specifically, which are generally smaller than 20 mm (0.79 inches)—too small for salvage facility tracking—particle tracking modeling was necessary to estimate Action 3 entrainment. BiOp at 282. The district court appears to have misunderstood the distinction between the analyses supporting the Action 1 and 2 limits and those supporting the Action 3 limits. San Luis & Delta-Mendota, 760 F. Supp. 2d at 922 (finding that the FWS derived the !5,000 cfs limit for Action 3 from the district court’s previous order, from a non-linear DWR model, and from Figure B-13, failing to mention the particle tracking modeling). The particle tracking modeling results are reflected in the BiOp’s figure B-16, and demonstrate a low risk of entrainment as long as OMR flows remain below !2,000 cfs, increasing to a 20 percent risk at !3,500 cfs. BiOp at 366–67. The high entrainment at more negative flows would potentially place at risk 80 percent of all larval/juvenile smelt over the period of the approximately four months that Action 3 is under effect. BiOp at 366. As the OMR flow approaches !5,000 cfs, the modeling predicts that smelt entrainment will near 100 percent at multiple a reviewing court to second guess the agency’s scientific judgments in such matters where, as is the case here, the agency’s determination appears to fall within the data-justified range. 27 Particle tracking modeling simulates larval fish by inserting neutrally buoyant particles into a model domain with flow conditions. SAN LUIS V. JEWELL 67 stations. BiOp at 367. Given such findings, the FWS did not act unreasonably in setting the OMR flow limits; the particle tracking modeling completed for Action 3 provides independent support for the OMR limits. Third, the DWR recommended to the FWS that it set the parameters at !5,000 cfs. In its informal comments to the FWS, it recommended that “[t]he justification for using a range of !5000 to !1250 cfs OMR rather than the !5000 cfs the FWS, USBR, and DWR proposed to the District Court last summer should be more clearly explained.” In formal comments submitted to Reclamation just two weeks before the BiOp was due, the DWR criticized the RPA Component 1 (Action 2) because it “call[ed] for much more restrictive Old and Middle River Flows than supported by the best available data to reasonably minimize entrainment of adult delta smelt.” As the DWR noted, the “proposed flows range from !1,250 cfs to !5,000 cfs. The current set flow of !5,000 cfs should be used except in specific circumstances relating to fish survey and salvage data, because it is better supported by the available scientific information.” (emphasis added) The DWR added that [i]n DWR’s October 16 submittal we presented the monthly analysis of Old and Middle River (OMR) flows and salvage of adult delta smelt that was also provided to Judge Wanger in 2007. This analysis appears to have been ignored by the FWS even though the analysis is more highly predictive of adult delta smelt salvage than any of the analysis presented by the FWS in the Draft BO. It clearly shows that when the monthly OMR 68 SAN LUIS V. JEWELL flows are more negative than about !5,000 cfs., the risk of salvage increases dramatically. .... Given the lack of clear relationship between salvage and population effects, any constraints on salvage needs to focus on avoiding peak entrainment events rather than attempting to eliminate salvage. Therefore, we again suggest that this analysis be included in the BO and that the !5,000 cfs OMR flows on a 14 day average period be used except in rare circumstances where the data indicates that less negative flows are needed to protect against peak entrainment events. (emphasis added). Fourth, in its comments on an early draft BiOp, the independent peer review also urged consideration of the relationship between salvage and OMR flows: “[The relationship between OMR flows and salvage] is a sound and valuable way to set targets to reduce entrainment. The USFWS also presents a reasonable regression analysis to determine the break-point in the OMR-salvage relationship . . . . The breakpoints determined by these analyses were used to justify the selection of target OMR flows.”28 It was not arbitrary and capricious for the FWS to rely on DWR’s own 28 At the same time, the DWR criticized the draft because “the analysis appeared to be well done but was poorly described and largely undocumented.” SAN LUIS V. JEWELL 69 expertise and views on the appropriate flow limits, even if it did not accept DWR’s preferred recommendation. Finally, we have one last observation in this area. When the district court remanded the 2005 BiOp as arbitrary and capricious—a BiOp that concluded that the operations of CVP and SWP would not adversely affect the delta smelt—the district court ordered Reclamation and DWR to “operate the CVP and SWP to achieve a daily average net upstream (reverse) flow in the OMR not to exceed [!]5,000 cfs on a seven-day running average.” San Luis & DeltaMendota, 760 F. Supp. 2d at 863–64. During the drafting of the BiOp, an interagency delta smelt team (known as the “Delta Smelt OCAP BO Technical Team”) “discussed the merits of using !5,000 OMR per Wanger Order rather than !3,500 OMR as recommended in the proposed draft action.” Notes of the meeting state: The !5,000 OMR cap was established by Wanger. The Team discussed the biological needs of the smelt to remove jeopardy and adverse modification. What might have worked in the year 2002 might not work correctly in 2009 because the population has crashed. It was suggested that scaling the amount of protection to the fall mid-water trawl (FMWT) (i.e. an estimate of abundance) would be helpful. .... If !5,000 OMR is the cap to protect adults, then the cap for Action #3 should be less than that, because the juvenile behave more like 70 SAN LUIS V. JEWELL particles. . . . OMR rates directly relate to zone of entrainment. If fish are really out of the central Delta then perhaps !5,000 OMR might provide sufficient protection. However, it is extremely difficult to determine when fish are out of South/Central Delta. The district court criticized the FWS for relying on “a provisional court order, entered as a remedial stopgap measure pending comprehensive scientific analysis.” San Luis & Delta-Mendota, 760 F. Supp. 2d at 896. We understand the district court’s concern and agree with the court’s general proposition that evidence used to support a “stopgap measure” is not, without more, substantial evidence supporting a final measure. But we also think the principle is not so easily applied in this case. As the district court noted, its 2007 order—in which the !5,000 cfs figure was found—was based on an evidentiary hearing in which two studies were introduced that considered the relationship between OMR flows and delta smelt salvage. Id. According to the district court, one of these studies was the basis for B- 13. The FWS can hardly be faulted for thinking that the district court’s acceptance of those studies and the issuing of an order with real-world consequences for people and smelt might present at least a prima facie case for the !5,000 cfs figure. We understand and agree with the district court that if the district court’s interim order was the sole basis for the FWS’s BiOp that it would not constitute substantial evidence. But, in 2008, the FWS was at least entitled to rely on the studies the district court had accepted (albeit on an interim basis) in 2007. The interagency team’s infelicitous reference to the “Wanger Order” was surely a shorthand for the SAN LUIS V. JEWELL 71 evidence on which the district court based its interim order, and not just the order itself. Additionally, we can take notice of the fact that by 2008, when the BiOp was issued, the FWS had a year’s experience living under the district court’s order, as the members of the interagency delta smelt team undoubtedly knew. Again, if the “Wanger Order” was the sole evidence in the record for the FWS’s RPA Component 2 (Action 3), we would not hesitate to find that the agency had not relied on the “best scientific data available,” but where the !5,000 cfs figure in the Wanger Order was one more data point for the agency, we cannot find that the agency’s reference is irrelevant or improper.
monitoring system that accounts for the smelt population as a whole29 As we have described, the FWS’s task of monitoring OMR flow and smelt population is a daunting one. The BiOp accounts for these challenges in a number of ways, including choosing conservative models so as to best meet its ESA obligations. One other way in which the BiOp addresses these practical difficulties is by integrating its various protections; the OMR flow limits exist as but one part of a complicated dynamic system. Another significant part of this system is a limit on the total allowable take of delta smelt: the incidental take limit (ITS). BiOp at 387. 29 We do not believe the BiOp’s OMR flow limits to be arbitrary and capricious for several alternative reasons previously described. But, even had the FWS relied entirely on Figures B-13 and B-14 in setting the OMR flow limits—it did not—and even if such total reliance would otherwise be arbitrary and capricious—it would not—we hold that the BiOp’s use of whole population numbers in the ITS fully supports its OMR flow limits. 72 SAN LUIS V. JEWELL The ITS influences OMR flow levels in two primary ways. First, the ITS is used to establish a “Concern Level” estimate to “help guide implementation of the RPA.” Id. By “indicat[ing when] salvage levels approach[] the take threshold,” the Concern Level acts “as an indicator that operations need to be more constrained to avoid exceeding the incidental take.” Id. If the Concern Level is actually reached, a meeting of the Smelt Working Group—a group comprised of interagency biologists who monitor delta smelt conditions and recommend OMR flow levels—is triggered. Id. Second, the ITS “functions as an action that influences operations under the RPA.” San Luis & Delta-Mendota, 760 F. Supp. 2d at 929. Pertinent to the present discussion, the ITS is used to set actual flow levels under the RPA in real time. BiOp at 352, 354, 357. The real-time actual flow levels are set and adjusted based on the input of the Smelt Working Group and Project operators—a group informed by the ITS. BiOp at 352, 354, 357. Thus, the actual OMR flow is highly influenced by the ITS, as these two provisions work together as part of the complex dynamic system established by the BiOp. This is relevant to the present discussion: even while the OMR flow limits, viewed in isolation, may not account for total smelt population in a manner acceptable to the district court, the ITS—and therefore the actual OMR flows—takes the total smelt population into account. The ITS establishes take limits that vary each year based on the preceding Fall Midwater Trawl index (FMWT), an abundance proxy for the delta smelt’s population size—and the proxy for population suggested both by the FWS and the court-appointed experts as they considered normalizing the data in B-13. BiOp at 287, 383–86. The ITS is calculated by SAN LUIS V. JEWELL 73 projecting future salvage from historic salvage in years with comparable flows to the RPA, BiOp at 384, and then scaling that number to overall abundance using the prior year’s FMWT, BiOp at 385. This procedure “yields a discrete value for take as salvage so that the adaptive process can operate relative to an estimate of the absolute number of fish extant in the system.” San Luis & Delta-Mendota, 760 F. Supp. 2d at 926–27. As such, the take limits are adjusted to reflect the best estimate of smelt’s existing population. BiOp at 354 (noting both salvage and population size as “important variables” in setting flow). Thus, in making real-time operational recommendations to implement the RPA, the BiOp relies on both raw salvage numbers and the whole smelt population. BiOp at 352. In conclusion, we agree that the FWS should have at least prepared a graph similar to B-13 based on normalized data or explained why it could not. Nevertheless, overall, its use of OMR flows is supported by substantial evidence in the record. As convoluted as the BiOp is, we can discern the path the agency took to arrive at the !5,000 cfs figure used in RPA Component 1 (Actions 1 and 2) and RPA Component 2 (Action 3). See Bowman Transp., Inc. v. Arkansas-Best Freight Sys., Inc., 419 U.S. 281, 286 (1974) (“[W]e will uphold a decision of less than ideal clarity if the agency’s path may reasonably be discerned.”). In the sense explained by Dr. Quinn, the BiOp’s choice of !5,000 cfs was arbitrary; that is, the FWS had to choose some number from a broad range—perhaps in the !4,000 cfs to !6,000 cfs range—and no graphs or charts were likely to give the FWS a precise number. In this sense, !5,000 cfs is arbitrary because no more precise number can be identified, and a number from the range must be selected; !5,000 is thus an arbitrary number because the FWS could also have chosen !4,999 or 74 SAN LUIS V. JEWELL !5,001 or some other number within the range. That !5,000 is arbitrary in this sense does not make the choice of !5,000 arbitrary in the sense captured by the APA; its decision was not the result of arbitrariness or caprice. The APA does not demand strict scrutiny. The FWS is charged with protecting the delta smelt, and it chose a reasonable figure. B. The 2008 BiOp’s Determination of X2 Was Not Arbitrary and Capricious The FWS found that Reclamation and DWR’s proposed operations “are likely to negatively affect the abundance of delta smelt” by “substantially decreas[ing] the amount of suitable abiotic habitat for delta smelt.” BiOp at 236–37. To address the loss of habitat, the FWS proposed in RPA Component 3 (Action 4) that in September and October, in years when the preceding precipitation and runoff period was defined as “wet or above normal,” Reclamation and the DWR must provide sufficient Delta outflow to maintain monthly average X2 no more eastward than 74 km from the Golden Gate in wet years and 81 km in “above normal” immediate water years. BiOp at 282, 369. The FWS had previously found that the amount and quality of spawning habitat available to delta smelt is linked to the location of X2. BiOp at 239–40. As we previously discussed, X2 represents the point in the Bay-Delta estuary where the salinity is two parts per thousand, and is the center point of the LSZ, which is considered suitable spawning habitat for the smelt. X2, in turn, depends on delta outflow, which is largely determined by the difference between the total inflow from the Sacramento and San Joaquin Rivers and the total amount of water exported through the Banks and Jones pumping SAN LUIS V. JEWELL 75 stations, which changes both annually and seasonally.30 BiOp at 236. As the BiOp found, “CVP/SWP operations control the position of X2 and therefore are a primary driver of delta smelt habitat suitability.” BiOp at 234. Because the location of X2 directly affects how much water can be exported to southern California for agricultural and domestic purposes, the determination of where X2 is located was critical to the parties. The district court found the BiOp was arbitrary and capricious with respect to the location of X2 on two grounds. First, the district court objected to the data the FWS used to locate X2. San Luis & Delta-Mendota, 760 F. Supp. 2d at 903–09. Second, it found that the actual location of X2 lacked support in the record. Id. at 910–13, 922–23. We will address each point in turn. 1. The FWS was not arbitrary and capricious in comparing DAYFLOW to CALSIM II The FWS used two sophisticated models to determine the impacts of past, present, and future operations of the Project on X2. The FWS chose a computer model called DAYFLOW, developed by DWR, to measure the historical environmental baseline, and a computer simulation model known as CALSIM II, developed jointly by DWR and Reclamation, to measure future operations. Id. at 896–98, 907. These models were used in a number of ways in the BiOp, and were important to the BiOp’s determination of the 30 It is more correct to think of X2 as a range of points rather than a single, fixed point in the estuary. As the BiOp points out, “X2 is strongly influenced by tidal cycles, moving twice daily up and downstream 6–10 km for its average daily location.” BiOp at 372. 76 SAN LUIS V. JEWELL impacts of Project operation on the location of X2. BiOp at 145.31 The DAYFLOW computer model uses past river flow, export pumping, precipitation, and estimated agricultural diversions from 1967–2007 to estimate the outflow from the Bay-Delta to the San Francisco Bay. This data was used by the BiOp as a “historic” baseline for X2. BiOp at 207. CALSIM II is a computer simulation model that uses Central Valley hydrology from 1922–2003 to simulate Project operations. Despite the fact that environmental regulation and non-Project water demands have historically changed, the CALSIM II model assumes that these factors are fixed in its modeling scenarios. BiOp at 207. This is in contrast with the DAYFLOW model, which, because it relies largely on actual reported data, does account for changes in regulations that impact water demand. DAYFLOW and CALSIM II also differ in other ways: for example, DAYFLOW uses historical data to provide daily simulations of operation whereas CALSIM II models Project operations on a monthly basis, and DAYFLOW calculates X2 location using a mathematical method known as the “KM” method whereas CALSIM II uses the “ANN” mathematical method. The BiOp, in analyzing the predicted location of X2, estimated that median X2 would move 10 to 15 percent farther upstream under the proposed action relative to the historic median X2 baseline. BiOp at 265; see also BiOp at 235. Appellees assert, however, that it is impossible to discern whether “this change was due to continued project operations into the future or whether the change was due to 31 Action 4, which involves the management of X2, was also significantly influenced by these models. SAN LUIS V. JEWELL 77 the modeling differences between the ‘historic’ Dayflowderived baseline and the Calsim II studies.” Appellees argue that a CALSIM II to CALSIM II comparison would have been preferable. The district court agreed, concluding that although the FWS had discretion to use a historical baseline model such as DAYFLOW, the FWS abused its discretion when it compared the two different models without discussing or accounting for the resulting bias. See San Luis & Delta-Mendota, 760 F. Supp. 2d at 902–05. Although the district court acknowledged that “no superior set of models have been identified,” it held that a calibration problem created by using DAYFLOW with CALSIM II made the FWS’s choice of methodology arbitrary and capricious and required correction or explanation. Id. at 877, 899, 909. We recognize that the CALSIM II to DAYFLOW comparison is not without its limitations, but hold that the FWS’s decision to use these two models together, even without further calibration, was not arbitrary and capricious.32 Because the FWS’s decision to use the DAYFLOW and CALSIM II models together is a “scientific determination,” Baltimore Gas & Elec. Co., 462 U.S. at 103, that “requires a 32 It is not clear from the record that the district court was correct in concluding that the FWS’s reliance on a comparison of CALSIM II and DAYFLOW models taints the BiOp’s conclusions regarding the impacts of Project operations on the location of X2, or regarding Action 4. See San Luis & Delta-Mendota, 760 F. Supp. 2d at 922–23. The FWS has indicated other possible bases for its conclusions. For example, the FWS refers to field sampling data, and not modeling results, as providing the basis for its conclusions about X2 conditions, at least in the fall. The BiOp also relies on historical data to demonstrate the upstream shift of fall X2 over time, BiOp at 237, 264, as well as the extent to which the Project operations influenced the upstream shift in fall X2, BiOp at 179–82, 236, 270. See also Note 33 infra. 78 SAN LUIS V. JEWELL high level of technical expertise,” Marsh, 490 U.S. at 377, we must be at our most deferential in reviewing this provision of the BiOp, Baltimore Gas & Elec. Co., 462 U.S. at 103. Therefore, the question, under the ESA is not whether the FWS should have conducted independent studies in lieu of DAYFLOW or CALSIM II, Sw. Ctr. for Biological Diversity, 215 F.3d at 60 (“[T]he Secretary has no obligation to conduct independent studies.”), but whether these models represent the “best scientific and commercial data [currently] available,” 16 U.S.C. § 1536(a)(2); Inland Empire Public Lands Council v. U.S. Forest Serv., 88 F.3d 754, 762 (9th Cir. 1996). Because we agree with the district court’s conclusion that “no superior set of models have been identified,” San Luis & Delta-Mendota, 760 F. Supp. 2d at 909, the only question is whether the FWS’s failure to calibrate the two models renders conclusions drawn from their evidence not merely “weak,” but “arbitrary and capricious.” Greenpeace Action v. Franklin, 14 F.3d 1324, 1336 (9th Cir. 1992) (noting that “the fact that the evidence [that an agency relies on] is ‘weak’” is not dispositive); see also Bldg. Indus. Ass’n of Superior Cal. v. Norton, 247 F.3d 1241, 1246–47 (D.C. Cir. 2001) (holding that the fact that the “studies the Service relied on were imperfect . . . alone is insufficient to undermine those authorities’ status as the ‘best scientific . . . data available’”). The FWS explained why it chose to use “a combination of available tools and data.”33 San Luis & Delta-Mendota, 33 The BiOp makes clear that the FWS did not rely exclusively on the CALSIM II/DAYFLOW comparison, but looked to previous government studies, and several peer-reviewed scientific studies: SAN LUIS V. JEWELL 79 760 F. Supp. 2d at 912; BiOp at 204–05. The BiOp explained that it used the DAYFLOW model as a baseline, rather than a different CALSIM II simulation, because when it compared a CALSIM II simulation of current Project operations to a CALSIM II simulation of past Project operations, it found that the two were “nearly identical,” despite the fact that past and current Project operations are significantly different. BiOp at 204–05. According to the BiOp, “changes were expected” between the two CALSIM II studies. BiOp at 204. Because that comparison did not yield significant differences, as the FWS anticipated, the BiOp concluded that “the CALSIM monthly simulation model does not capture a precise Delta operation.” BiOp at 204. These “inaccuracies in CALSIM [lead the FWS] to use actual data to develop an empirical baseline.” BiOp at 206. The FWS chose “the DAYFLOW database . . . and OMR data obtained from USGS.” BiOp at 206. The BiOp also noted that the CALSIM II simulation provided “an imperfect representation of the pre-POD [pelagic organism decline]” environmental situation and that supplemental analysis was needed “to compensate for this modeling limitation.” BiOp at 205. In This analysis of the effects of proposed CVP and SWP operations on the delta smelt and its critical habitat uses a combination of available tools and data, including the CALSIM II model outputs provided in the appendices of Reclamations’ 2008 biological assessment, historical hydrologic data provided in the DAYFLOW database, statistical summaries derived from 936 unique 90-day particle tracking simulations published by Kimmerer and Nobriga (2008), and statistical summaries and derivative analyses of hydrodynamic and fishers data published by Feyrer et al. (2007), Kimmerer (2008), and Grimaldo et al. (accepted manuscript). BiOp at 204. 80 SAN LUIS V. JEWELL other words, when the FWS used CALSIM II as both a baseline and as a predictor, it appeared to yield inaccurate results, so the FWS used the next best available baseline: the DAYFLOW model. Thus, the FWS explained: the “[CALSIM II] Study 7.0 was the model run that Reclamation and DWR thought best represented current operations, and was thus intended as a ‘current baseline.’ However, due to limitations of CALSIM II to accurately model actual operations, [the FWS] also used the 1967–2007 DAYFLOW summaries . . . to compare against CALSIM II outputs.” BiOp at 207. The district court acknowledged that “[t]he theoretical problems with using a Calsim II to Calsim II comparison were manifest,” yet it found that the “FWS’s decision to use a Calsim II to Dayflow comparison . . . without attempting to calibrate the two models . . . was arbitrary and capricious and ignored the best available science showing that a bias was present.” San Luis & Delta-Mendota, 760 F. Supp. 2d at 906–07. The evidence for the modeling bias in the CALSIM II/DAYFLOW comparison was not clearly identified in the comments sent to the FWS before it issued the BiOp, and the comments largely recommended correcting the bias by comparing one CALSIM II model to another CALSIM II model—precisely the comparison the FWS found flawed and inaccurate. For example, San Luis & Delta-Mendota Water Authority, one of the plaintiffs-appellees here, submitted extensive and thoughtful comments on a portion of the draft BiOp and peer review analysis. The water authority expressed its concern that it was “methodologically inappropriate to compare historical data to simulated data. Simulated data must be compared to simulated data.” The DWR advised the FWS of some of the same problems. In its comments on a portion of the draft BiOp, the DWR pointed SAN LUIS V. JEWELL 81 out that the model made assumptions that might not be borne out in the historical data and that “[g]reat caution should be taken when comparing actual data to modeled data.” Like the water authority, it too recommended that CALSIM II modeling “be used to compare one set of model runs to another.” In later comments, the DWR repeated its concern that the BiOp “compared model runs of future operation with historical conditions” and suggested that “[i]mpact analyses often compare only model scenarios to avoid these . . . problems.” The independent peer review panel also expressed some concern with comparing the historical baseline with the CALSIM II simulated results. It pointed out that the “large difference between [CALSIM II] results and the historical baseline conditions defined with data can confuse the comparisons of metrics . . . between a simulated study and historical baseline.” The panel suggested that “[i]deally, a model-simulated baseline should be available that is consistent with the historical data . . . . It is unfortunate that model-generated baselines with a high degree of reliability were not made available for this analysis.” The post-hoc views of the court’s experts reflect some similar concerns. Dr. Quinn agreed that a comparison between models with identical databases and assumptions was preferable, but advised that “[a]s long as we bear in mind the fact that these are two very different models, I do not see why we cannot compare them.” After discussing some of the data provided to him, he concluded that the outputs from the two models cannot be used interchangeably for estimating either X2 (in km) or flow (in cfs). This does not reflect any criticism of either model. Their inner workings are apparently quite different, as are 82 SAN LUIS V. JEWELL their fundamental purposes, as explained to us. However any comparisons between them must explicitly account for the differences. Similarly, Dr. Punt stated that [i]n principle, there is nothing wrong with fitting a model using a set of OMR/X2 values from one model and making predictions using OMR/X2 values which are based on the output from a different model, as long as the two sets of values are calibrated . . . . Dr. Punt noted that the FWS had articulated its assumptions and explained why he thought the FWS should have performed additional calibration: It is recognized in the record that the modeled X2 does not reflect the “historical” X2 (BiOp Figure E-28), and the BiOp does compare historical and CALSIM-predicted X2 values by month (Figure E-26). However, the BiOp does not make this comparison for comparable years. Failure to attempt examination of whether it is necessary to calibrate the historical data and the CALSIM output would not normally be considered appropriate scientific practice in the field. (internal citations omitted). We recognize that the FWS’s decision to compare its chosen baseline, produced by DAYFLOW, to its future projection, produced by CALSIM II, was not perfect—as SAN LUIS V. JEWELL 83 everyone has acknowledged. We nevertheless hold that this comparison was not arbitrary and capricious. The BiOp explained its assumptions and explained why it rejected the CALSIM-to-CALSIM comparison suggested by the DWR and others who reviewed the draft BiOp. The fact that the FWS chose one flawed model over another flawed model is the kind of judgment to which we must defer. As we said in Environmental Defense Center, Inc. v. EPA: “We defer to an agency decision not to invest the resources necessary to conduct the perfect study, and we defer to a decision to use available data unless there is no rational relationship between the means [the FWS] use[d] to account for any imperfections in its data and the situation to which those means are applied.” 344 F.3d 832, 872 (9th Cir. 2003) (citations omitted). “The existence of a flaw . . . does not require us to hold that the agency’s use of the model was arbitrary.” Am. Iron & Steel Inst. v. EPA, 115 F.3d 979, 1005 (D.C. Cir. 1997) (per curiam). Rather, we will “reject an agency’s choice of a scientific model ‘only when the model bears no rational relationship to the characteristics of the data to which it is applied.’” Nat’l Wildlife Fed’n v. EPA, 286 F.3d 554, 565 (D.C. Cir. 2002) (quoting Appalachian Power Co. v. EPA, 135 F.3d 791, 802 (D.C. Cir. 1998)); accord Envtl. Def. Ctr., 344 F.3d at 872. That is not the case here. The district court’s determination to the contrary was heavily influenced by experts supplied by the parties which, for the reasons that we have explained, was inappropriate. The record is less certain than the district court was willing to admit.34 That 34 The district court refers to testimony from the parties’ experts in terms that overstate what the record will bear. See, e.g., San Luis & DeltaMendota, 760 F. Supp. 2d at 909 (“undisputed expert testimony”), id. (“[A]ll experts in this case agree”), id. at 912 (“[u]ndisputed expert 84 SAN LUIS V. JEWELL appellees were able to produce post-hoc theories alluding to the possibility of bias from such a comparison does not invalidate the FWS’s choices where there is no indication that a failure to calibrate would do more than add an uncertainty factor to the results, as well as no indication that a de-biasing calibration is technically feasible. Under our deferential standard of review, we therefore hold that the FWS’s choice and use of models was based on the best available science. Ideally, the FWS would have thoroughly discussed its reasoning with regard to possible issues arising from the use of DAYFLOW with CALSIM II. But, the fact that the FWS’s explanation for its choices does not fully address every possible issue that flows from that choice does not render the FWS’s determination unreasonable or unsupported. We do not require agencies to analyze every potential consequence of every choice they make; to do so would put an impossible burden on agencies. Rather, “we review all agency choices with respect to models, methodologies, and weighing scientific evidence” to ensure that the agency’s “choices [are] supported by reasoned analysis.” Ecology Ctr. v. Castaneda, 574 F.3d 652, 665 (9th Cir. 2009). Particularly in an area as “unwieldy and sciencedriven” as this, the FWS’s statistical modeling “does not easily lend itself to judicial review.” Appalachian Power, 135 F.3d at 802; see id. (“Statistical analysis is perhaps the prime example of those areas of technical wilderness into which judicial expeditions are best limited to ascertaining the lay of the land.”). “[T]hat some or many [experts] would disapprove [of the FWS’s] approach does not answer the question presented to us. In reviewing [the FWS’s BiOp], we testimony”), id. at 922 (“the Calsim II to Dayflow comparison has the potential to introduce significant, if not overwhelming, bias”). SAN LUIS V. JEWELL 85 do not sit as a panel of referees on a professional [scientific] journal, but as a panel of generalist judges obliged to defer to a reasonable judgment by an agency acting pursuant to congressionally delegated authority.” City of L.A. v. Dep’t of Transp., 165 F.3d 972, 977 (D.C. Cir. 1999). In this instance, the FWS has provided a reasoned analysis explaining why the DAYFLOW model was used as the baseline. Consequently, we hold that the FWS did not act arbitrarily and capriciously in relying on the CALSIM II and DAYFLOW models in evaluating the impacts of Project operations on the location of X2, or in justifying Action 4. 2. The BiOp sufficiently explained the fall X2 locations The district court held that the BiOp “fails to explain why it is essential to maintain X2 at 74 km and 81 km, respectively, as opposed to any other specific location.” San Luis & Delta-Mendota, 760 F. Supp. 2d at 923. Specifically, the court found the “Federal Defendants have not identified any record evidence that provides such an explanation. This total lack of explanation violates the APA[].” Id. at 922. With respect to the latter finding, the record is contrary to the district court’s finding. There is record support for the BiOp’s proposal. Whether the record will “explain why it is essential to maintain X2 at 74 km and 81 km . . . as opposed to any other specific location” is a different question. In fact, it is the wrong question. As we have previously explained, as the combined pumping operations of the SWP/CVP remove hundreds of thousands of gallons of fresh water from the Bay-Delta, X2—the salinity-defined location of the smelt’s primary spawning habitat—shifts eastward towards the delta. BiOp at 86 SAN LUIS V. JEWELL 373. As the ocean’s salty influence encroaches further upstream, it mimics drought conditions in the Bay-Delta, regardless of the previous season’s precipitation. BiOp at 271, 273–74 (plotting spring and fall X2 locations). Among other things, this has resulted in an increasing divergence between spring and fall X2 locations. The BiOp determined that the “long-term upstream shift in X2 during fall has caused a long-term decrease in habitat area availability for delta smelt,” and it set forth an adaptive management program to minimize the effects of Project pumping on X2: RPA Action 4. BiOp at 374. Action 4 targets the fall location of X2 during “wet” and “above normal” years: “the years in which project operations have most significantly adversely affected fall [X2 location].” BiOp at 373. Specifically, Action 4 requires that fall X2 be maintained at a location no greater than 74 km upstream from the Golden Gate Bridge following “wet” water years, and no greater than 81 km upstream following “above normal” water years. BiOp at 282–83. The BiOp includes a number of different explanations, both specific and general, for RPA Component 3 (Action 4)’s regulation of X2 location. First, as discussed in the previous section, the FWS used CALSIM II and DAYFLOW to give it a picture of where X2 was located. Those models predicted different values for the location of X2 based on differing assumptions. DAYFLOW relied on historical data, while CALSIM II predicted where X2 might be located based on future Project operations. The FWS found that the two models offered different estimates of X2 location, and that, in general, “CALSIM II modeled scenarios were 10–15 percent further upstream than actual historic X2.” BiOp at 235. Thus, “[m]edian historic fall X2 was 79 km, while median values for the CALSIM II modeled scenarios ranged from 87 SAN LUIS V. JEWELL 87 to 91 km. The CALSIM II modeled scenarios all had an upper range of X2 at about 90 km.” BiOp at 235. The FWS generated numerous figures and charts with information taken from these models, many of which graphed X2 location as a function of another variable. See, e.g., BiOp at 260–61, 265, 270, 281, 282, 283, 284, 285. The FWS’s graphing range for X2 location was 60–95 km, with most data points between 65–85 km, and a significant cluster between 70–85 km. BiOp at 270. Second, the data from the models was consistent with the FWS’s written finding that “[d]uring the past 40 years, monthly average X2 has varied from as far downstream as San Pablo Bay (45 km) to as far upstream as Rio Vista on the Sacramento River (95 km). . . . In general, delta smelt habitat quality and surface area are greater when X2 is located in Suisun Bay.” BiOp at 191. Elsewhere in the BiOp, the FWS found based on an outside scientific study that “prior to spawning entrainment vulnerability of adult delta smelt increased at the SWP and CVP when X2 was upstream of 80km.” BiOp at 219. Third, the BiOp pointed out that X2 varies in response to a number of factors, both natural and man-made. The natural factors include the tides and the spring inflow. The BiOp found that “X2 is strongly influenced by tidal cycles, moving twice daily up and downstream 6–10 km from its average daily location.” BiOp at 372. The spring runoff affects X2 as well. The BiOp found that “very high spring outflows have always pushed X2 far downstream resulting in delta smelt distributions distant from the influence of Banks and Jones.” BiOp at 221. Aside from CVP/SWP’s operations, there are other man-made factors to consider. For example, the Suisun Marsh Salinity Control Gates have historically been 88 SAN LUIS V. JEWELL operational for anywhere from 10 to 120 days a year. BiOp at 218. Operation of the salinity control gates may shift X2 as far as 3 km upstream. BiOp at 218; see also BiOp at 241, 243–44. The BiOp noted that the 3 km upstream shift was preferable to the “10–20 km shifts that have occurred for up to 120 or more days per year during late summer through early winter due to South Delta diversions.” BiOp at 219. Fourth, the BiOp also points out that not all habitats in the Bay-Delta are equally suited to the smelt, so that as X2 location shifts, the smelt may be affected by other factors as well. For example, as X2 location shifts, the delta smelt encounter changes in agricultural runoff; changes in turbidity, which affects smelt feeding and predation; and exposure to predators. E.g., BiOp at 372 (“the daily fluctuation in X2 around an upstream point such as Brown’s Island confines the [smelt] population to narrow channels, where delta smelt may be exposed to more stressors (e.g., agricultural diversions, predation) relative to a downstream X2”); BiOp at 238 (“[T]he eastward movement of X2 will shift the distribution of delta smelt upstream, and provide environmental conditions for nonnative fishes that thrive in stable conditions.” (internal citation omitted)). Thus, the BiOp points out that although CVP/SWP operations remain the most important factor affecting smelt habitat, BiOp at 177–79, “there is no single driver of delta smelt population dynamics,” BiOp at 238; see also BiOp at 202 (referring to “the multitude of factors that affect delta smelt population dynamics”). The smelt habitat is a complex and dynamic system. Fifth, because X2 varies not only with CVP/SWP’s operations, but with natural phenomena beyond the control of the agencies—such as the tides, the seasons, and the annual SAN LUIS V. JEWELL 89 rainfall—the FWS had a range of choices for X2. Its goal was to “move the habitat away from Delta impacts and into broader open waters west of Sherman Island.”35 BiOp at 373. Rather than trying to define a single point for X2, the FWS chose a range, and refined the range by whether the year was “wet” or “above normal.” We think there was ample evidence in the record to support the choice of X2 between 74 and 81 km, depending on the season. The district court’s finding that there was a “total lack of explanation” is belied by the record.36 It appears the district court focused on whether there was support in the record for the FWS’s choice of 74 km, as opposed to 73 km or 75 km, hence its decision to question whether the FWS had adequately explained “why it is essential to maintain X2 at 74km and 81km . . . as opposed to any other specific location.” San Luis & Delta-Mendota, 760 F. Supp. 2d at 923. This was not the district court’s responsibility. The ESA provides that Reclamation had to seek the FWS’s opinion to ensure that its operations were “not likely to jeopardize the continued existence of any endangered species or threatened species or result in the 35 Sherman Island lies at the confluence of the Sacramento and San Joaquin Rivers. The 74–81 km range selected by the FWS lies west of Sherman Island. 36 The district court’s conclusion is especially puzzling because it accepted a similar FWS finding in its prior decision. See Kempthorne, 506 F. Supp. 2d at 335, 380 (noting that a survey of delta smelt abundance “increases dramatically whenever X2 is located between Chipps and Roe islands,” and that “[w]hen X2 is located upstream of Chipps Island, smelt are vulnerable to entrainment and are located in an area that is not ideal for feeding or protection”) (citing the administrative record). See also BiOp at 114, 132. 90 SAN LUIS V. JEWELL destruction or adverse modification of habitat of such species.” 16 U.S.C. § 1536(a)(2). It is the FWS’s responsibility to set forth its opinion and “those reasonable and prudent alternatives which [the Secretary of the Interior] believes would not violate [§ 1536(a)(2)] and can be taken by [Reclamation].” Id. § 1536(b)(3)(A). A “reasonable and prudent alternative” is a flexible standard for the consulting agency; it is not the equivalent of the “least restrictive alternative,” which is the way the district court treated the inquiry. Sw. Ctr. for Biological Diversity, 143 F.3d at 523. Under the ESA, the consulting agency is “not required to explain why he chose one RPA over another,” nor is it “required to pick the best alternative or the one that would most effectively protect the [species] from jeopardy.” Id. Rather, the FWS “need only have adopted a final RPA which complied with the jeopardy standard and which could be implemented by the agency.” Id. Accordingly, it was error for the district court to hold that the FWS must “explain why it is essential to maintain X2 at 74 km and 81 km . . . as opposed to any other specific location.” San Luis & DeltaMendota, 760 F. Supp. 2d at 923. Even if we thought the ESA demanded the kind of precision insisted on by the district court, we believe there is an explanation in the record for the FWS’s choice of 74 km and 81 km as its seasonal parameters. And the explanation is both logical and simple: At 74 km there is a monitoring station for the Bay-Delta at Chipps Island; at 81 km there is a monitoring station at Collinsville. BiOp at 114, 132. In its comments to the FWS, the DWR questioned the feasibility of locating fall X2, “especially if the spring X2 location is SAN LUIS V. JEWELL 91 significantly west of Port Chicago.”37 The DWR was concerned with how the agencies would measure compliance with any standard selected by the FWS and pointed out that “it would be difficult to measure an X2 at 85 km, whereas it would be much easier to measure at Collinsville (81 km) or Emmaton (92 km).” Where X2 represents a range of choices, choosing an X2 that can be measured and enforced is a perfectly rational response. See Sw. Ctr. for Biological Diversity, 143 F.3d at 523. We recognize that the stakes are exceedingly high in this case, but we conclude that the FWS’s choice, given the record, represented a reasonable and prudent alternative. C. The BiOp’s Incidental Take Statement Is Not Flawed When the implementation of an RPA will cause “incidental take” of a species, defined as “takings that result from, but are not the purpose of, carrying out an otherwise lawful activity conducted by the Federal agency or applicant,” 50 C.F.R. § 402.02, the agency is required to provide an incidental take statement (ITS) that “[s]pecifies the impact, i.e., the amount or extent, of such incidental taking on the species,” 50 C.F.R. § 402.14(i)(1)(i); see 16 U.S.C. § 1536(b)(4)(C)(i). The FWS determined that “take of the delta smelt is likely to occur in the form of kill, capture (via salvage), wound, harm, and harass as a result of CVP/SWP operations within the action area,” BiOp at 286, and prepared an ITS, BiOp at 285–310. In the ITS, the FWS provided separate take limits for juvenile and adult smelt, noting that “individuals of the larval/juvenile lifestage are 37 The FWS did not set its fall X2 target at Port Chicago, which is west of the 74 km marker proposed by the FWS. 92 SAN LUIS V. JEWELL less demographically significant than adults.” BiOp at 289. The FWS used different data sets in determining these separate limits. As the ITS notes, “[t]he mean values from 2005–2008 were used as an estimate of [juvenile] take under the RPA,” BiOp at 289, while “[t]he average[] value for [water years] 2006 to 2008 was used to calculate prespawning adult delta smelt, BiOp at 287. In other words, the year 2005 was used in calculating the juvenile, but not the adult, take limit. BiOp at 287, 289. The ITS also acknowledges its limitations: there are numerous uncertainties inherent in the measurement of smelt take, and “salvage data (our most definitive measurement endpoint) reflects only a portion of the total mortality associated with entrainment.” BiOp at 383. The district court concluded that the BiOp’s ITS was arbitrary or capricious for two reasons: First, the district court found that the FWS failed to explain adequately its decision to use 2005–2008 salvage data when setting the incidental take limit for juvenile smelt, but to only use 2006–2008 salvage data when setting the incidental take limit for adult smelt. San Luis & Delta-Mendota, 760 F. Supp. 2d at 925–28. Second, the district court found that the FWS failed to explain its decision to rely on an average cumulative salvage index when it set a “Concern Level,” which, when triggered, requires the Smelt Working Group to make “an immediate specific recommendation to the [FWS].” BiOp at 387; see San Luis & Delta-Mendota, 760 F. Supp. 2d at 928–29. We hold that the ITS is not arbitrary and capricious because it includes adequate explanation and support for its determinations. SAN LUIS V. JEWELL 93 1. The ITS reasonably uses different data sets for adult and juvenile take limits First, the district court objected to the FWS’s choice of data. The ITS’s use of separate data sets to calculate the separate incidental take limits for juvenile and adult smelt is adequately explained in the ITS. In discussing the use of “[t]he mean values from 2005–2008” to calculate juvenile take limits, the ITS explains that “[t]he reason for selecting this span of years is that the apparent abundance of delta smelt since 2005 as indexed by the 20-mm Survey and the [Summer Townet Survey] is the lowest on record.” BiOp at 289. Accordingly, “[i]t was necessary to separate out this abundance variable, but also to account for other poorly understood factors relating salvage to OMR, distribution, and the extant conditions.” BiOp at 289. As noted elsewhere in the BiOp, “[i]n contrast to adult delta smelt, there is no well established index of larval and juvenile abundance to reliably scale the take of this lifestage to abundance. . . . This should be kept in mind . . . .” BiOp at 389. Therefore, faced with a greater degree of uncertainty in calculating juvenile incidental take, the FWS chose, conservatively, to incorporate an additional year of data—a year in which smelt abundance was “the lowest on record.”38 BiOp at 389–90. The BiOp is clear that it used 2006–2008 data for adults because “these years within the historic dataset best approximate expected salvage,” BiOp at 287, and 2005–2008 data for juveniles because juvenile smelt “are less demographically significant than adults,” BiOp at 289, and therefore a larger data set 38 This determination came on the heels of the district court’s invalidation of the 2005 BiOp’s ITS based on its failure to consider record-low population abundance in setting take limits. See San Luis & Delta-Mendota, 760 F. Supp. 2d at 918. 94 SAN LUIS V. JEWELL would be preferable.39 Such a decision to use a more conservative data set, when necessary, is exactly the sort that we afford agencies discretion to make. See, e.g., Fishermen’s Finest, Inc. v. Locke, 593 F.3d 886, 893, 896–97 (9th Cir. 2010) (upholding agency’s decision to rely on some data while disregarding other data). 2. The FWS reasonably uses an average cumulative salvage index Second, the district court objected to the FWS’s decision to use an average cumulative salvage index to create its “Concern Level.” BiOp at 387, 389–90. The Concern Level “indicate[s] salvage levels approaching the take threshhold.” BiOp at 387; see also BiOp at 289 (noting that the estimated number represents a concern level where “entrainment has reached high enough numbers to indicate the need for more protective OMR restrictions.”). When the Concern Level is reached, two actions follow: The Smelt Working Group must convene and make “an immediate specific recommendation to the [FWS],” and OMR flows may have to be adjusted “to a more restrictive level.” BiOp at 387. The Concern Level does not trigger automatic restrictions in OMR flows. It “may require” a reduction “unless available data indicate some greater level of exports is possible without increasing entrainment.” BiOp at 387 (emphasis added). The FWS’s decision to use an average in setting the incidental take limits is, like its choice of data sets, a choice 39 For similar reasons we disagree with the district court that the FWS must explain why it decided to include 2006 when it calculated larval/juvenile abundance. See San Luis & Delta-Mendota, 760 F. Supp. 2d at 917–18. SAN LUIS V. JEWELL 95 entitled to substantial difference. See The Lands Council v. McNair, 537 F.3d 981, 991 (9th Cir. 2008) (noting that it is “well-established law” that courts owe deference “to agencies and their methodological choices”). As appellees note, the ITS’s “use of averaging . . . provides an ‘estimate’ of expected take that, based on the historical record of salvage, when applied[,] would likely be exceeded in many years.” In other words, appellees object to the fact that the ITS’s choice of methodology results in too restrictive of a management regime. The district court agreed, finding that “[t]he record does not explain why an ‘averaging’ approach was used.” San Luis & Delta-Mendota, 760 F. Supp. 2d at 929 (“Based on known adverse water supply consequences of operating the Projects in a ‘constrained’ manner, it is inexplicable that the FWS did not provide a clear and rational explanation of how the ITS is set.”). We hold that the record offers an adequate explanation. As with its decision to use an additional year of data when calculating the juvenile take limit, the ITS’s use of an averaging methodology “counteract[s] the uncertainties” inherent in its analyses by “overestimat[ing]” known parameters. See Baltimore Gas & Elec. Co., 462 U.S. at 103. In the context of the ITS’s repeated recognition of the “difficult[y] [in] definitively project[ing]” the “specific level of take of adult delta smelt at the CVP/SWP pumping facilities . . . due to inherent uncertainties,” BiOp at 383, the FWS’s use of an averaging methodology—that, by its nature, yields conservative limits that otherwise would have been exceeded in eleven of the past sixteen years, San Luis & Delta-Mendota, 760 F. Supp. 2d at 929—is a discernable, and justified path. See Bowman Transp., 419 U.S. at 285–86; see also Nw. Coal. for Alternatives to Pesticides, 544 F.3d at 1050 (upholding an agency’s discernable reliance on models 96 SAN LUIS V. JEWELL that “yield conservative data [where] the models incorporate[d] the higher of [the potential values] in assessing the overall risk”). Moreover, the establishment of a Concern Level is an enforcement norm, and thus a policy decision. “[S]election of an action level is primarily a legislative policy decision that we will uphold so long as it was reasonably drawn from the record.” Public Citizen Health Res. Group v. Dep’t of Labor, 557 F.3d 165, 184 (3d Cir. 2009). Ultimately, the appellees’ objection is not really to the record support for the Concern Level, but to the Concern Level itself. The choice of the Concern Level is quintessentially one within the discretion of the agency, and we have no basis for disturbing it here. D. The Record Supports the BiOp’s Conclusions Regarding the Indirect Effects of Project Operations The FWS is required to take into account both the “direct and indirect effects of an action on the species or critical habitat” when determining whether an action is likely to cause jeopardy. 50 C.F.R. § 402.02; see also 16 U.S.C. § 1536(a)(2). “Indirect effects” are “those that are caused by the proposed action and are later in time, but still are reasonably certain to occur.” 50 C.F.R. § 402.02. Here, the BiOp identified a number of indirect effects on the delta smelt from Project operations. Among other things, it concluded that Project operations were reasonably certain to (1) limit delta smelt food supply, and (2) increase harmful pollution and contaminants. It also noted (3) the harmful indirect effects that would likely spring from three “other stressors”: predation, aquatic macrophytes, and microcystis. See BiOp at 182–88, 202. SAN LUIS V. JEWELL 97 The BiOp found that a “multitude of factors . . . affect delta smelt population dynamics including predation, contaminants, introduced species, entrainment, habitat suitability, food supply, aquatic macrophytes, and microcystis.” BiOp at 202. It concluded that “[t]he extent to which these factors adversely affect delta smelt is related to hydrodynamic conditions in the Delta, which in turn are controlled to a large extent by CVP and SWP operations.” BiOp at 202. It noted that there were other sources of water diversion that affect smelt through entrainment, but that, even when “taken together,” they did not “approach[] the influence of the Banks and Jones export facilities.” BiOp at 202. Although the BiOp candidly assessed that there was “no single primary driver of delta smelt population dynamics” and that there were “non-CVP/SWP factors,” it ultimately concluded that “the CVP and SWP are a primary driver of delta smelt abiotic and biotic habitat suitability, health, and mortality” and that CVP/SWP operations “have also played an indirect role in the delta smelt’s decline by creating an altered environment in the Delta that has fostered the establishment of non-indigenous species and exacerbates these and other stressors that are adversely impacting delta smelt.” BiOp at 202–03. The district court was not persuaded, finding that “[t]he record does not support the BiOp’s conclusion that food web and pollutants/contaminant impacts are indirect effects of Project operations.” San Luis & Delta-Mendota, 760 F. Supp. 2d at 969. The district court also separately noted that “the BiOp’s conclusions about the causal connections between Project operations and ‘other stressors’ are ambiguous,” and thought the effects were “unsupported by record evidence and/or explanation” Id. We address each of these indirect effects in turn, and conclude that the BiOp analysis was 98 SAN LUIS V. JEWELL sufficiently clear and thorough so as not to be arbitrary and capricious, and that it was based on the best available science. See Kern, 450 F.3d at 1080–81. 1. Project operations indirectly affect smelt food supply First, the BiOp explores the likely impact of Project operations on smelt food supply. The BiOp describes “entrainment of Pseudodiaptomus forbesi (P. forbesi), the primary prey of delta smelt,” as a primary effect that “will adversely affect delta smelt.”40 BiOp at 203–04. Plainly stated, delta smelt appear severely food limited much of the time. P. forbesi is the smelt’s principal food source from summer to early fall, and so any Project impact on the availability of P. forbesi represents a threat to the smelt. See BiOp 228, 380. The BiOp concludes that such a threat is present: high water exports reduce flows that would otherwise transport P. forbesi into the delta smelt’s habitat, thereby contributing to smelt mortality and population declines. BiOp at 184–85, 228. Moreover, as water is pumped from the south Bay-Delta during June through September, P. forbesi are entrained in pumping stations, thereby reducing the overall availability of P. forbesi in the delta. BiOp at 228. Because “statistical evidence suggest[s] that the cooccurrence of delta smelt and [prey such as P. forbesi] has a strong . . . influence on the survival of young delta smelt from summer to fall,” BiOp at 228, the BiOp concluded that the Project’s effects on the P. forbesi population indirectly threaten the viability of delta smelt. 40 The FWS proposed to address this through Action 6, which requires the creation or restoration of 8,000 acres of habitat in the Delta and in Suisun Marsh. BiOp at 381. SAN LUIS V. JEWELL 99 We consider whether this analysis is supported by the best available science, and whether the FWS acted arbitrarily and capriciously in concluding, to a “reasonable certainty,” that Project operations indirectly affect delta smelt through its food supply. See 50 C.F.R. § 402.02. As both the district court and the peer review panel indicated, the FWS’s analysis suffers from some methodological limitations; some of the analyses underlying the agency’s conclusions involved extrapolating from a few sampling sites, and therefore may have overstated the scarcity of P. forbesi. The district court seized on the peer review panel’s comments, which pointed out: “Rather than correct this problem, FWS’s response was to abandon the quantitative analysis, choosing to advance the same, potentially flawed conclusion in a more subjective, qualitative analysis. This conduct suggests another unlawful, results-driven choice, ignoring best available science.” San Luis & Delta-Mendota, 760 F. Supp. 2d at 940. We think the district court’s criticism is incorrect. The independent peer review panel commented directly on the FWS’s analysis of the impact of CVP/SWP on P. forbesi and the BiOp’s conclusion that the abundance of P. forbesi “may vary inversely with export flow . . . and directly with outflow.” It stated: “The [p]anel agrees with this conceptual model and with the justification of its elements, which are well-supported.” The panel offered technical suggestions and then commented, “the figures meant to support this analysis are not convincing.” The panel then “suggest[ed] that this analysis be redone with the above considerations in mind. If [the] revised analysis does not show a substantial (not necessarily statistically significant) pattern, the analysis should be mentioned but the results dropped as quantitative metric from the EA.” (emphasis added). The FWS did exactly as the panel recommended. It 100 SAN LUIS V. JEWELL omitted the statistical analysis as justification for its conclusion. We cannot see the error in the FWS following the recommendation of the peer review panel. Moreover, the FWS had other reasons, explained in the record, for concluding that CVP/SWP operations had an effect on P. forbesi and that the abundance of P. forbesi indirectly affects the delta smelt. See BiOp at 184–85, 228. That it omitted a statistical study because it did not have sufficient data to justify it does not strike us as a “results-driven choice,” but as responsible science. Nothing in the ESA compelled the FWS to conduct the particular study the peer review panel thought inadequately supported by the data, and nothing in the peer review panel’s comments even hints that the statistics suggested a contrary conclusion—there is no evidence here to suggest that the FWS, in conspiracy with the peer review panel, was trying to hide evidence. Even if we thought that a “rigorous, large-scale study . . . would be preferable,” we have no authority to compel one: “in the absence of such a study,” even “credible anecdotal evidence” can “represent[] the best scientific . . . data available.” Nw. Ecosystem Alliance v. FWS, 475 F.3d 1136, 1147 (9th Cir. 2007) (internal quotation marks omitted). Like the peer review panel in question, we conclude that although the FWS’s analysis here is not without its limitations, it is “based upon the best available science.” See Nw. Ecosystem Alliance, 475 F.3d at 1147. Similarly, we are persuaded that the BiOp’s conclusion that Project operations indirectly affect the delta smelt by impacting the smelt food supply was “well supported.” We need not independently conclude that this conclusion is well supported to a “reasonable certainty.” It is sufficient that we conclude that SAN LUIS V. JEWELL 101 the BiOp’s conclusion was sufficiently supported such that the FWS did not arbitrarily and capriciously find this to be an indirect effect to a reasonable certainty. 2. Project operations indirectly affect the smelt through water contamination Second, the BiOp explores the Project’s impact on water contamination and therefore, indirectly, on smelt population viability. The BiOp first concludes that water contamination poses a threat to the delta smelt. Smelt throughout the Delta are exposed to various pesticides and contaminants, which “may affect embryo survival or inhibit prey production.” The BiOp singles out ammonia released from a waste processing facility in Sacramento and pesticides from agricultural operations. BiOp at 153, 187, 237. The BiOp observes that “concern over contaminants in the Delta is not new” and refers to mercury, selenium, pesticides, herbicides, ammonium concentrations, and undiluted drainwater. BiOp at 187. Reclamation observes that the “delta smelt are highly sensitive to high levels of ammonia” and that such contaminants are detrimental to the health of the smelt population because they render smelt susceptible to disease. BiOp at 187–88. The BiOp concludes that Project operations will dangerously increase the impact of contaminants on the smelt. One reason is topographical: as Project operations constrain the smelt habitat to smaller rivers and estuaries, the smelt’s overall exposure to these contaminants, which result primarily from land runoff, increases. See BiOp at 153. Relatedly, when Project operations reduce overall suitable habitat, the impact of contaminants on the smelt in the remaining habitat become intensified. The BiOp analyzes 102 SAN LUIS V. JEWELL this latter effect in the context of recognizing the importance of fall X2 to delta smelt viability: The FWS described two “[p]otential mechanisms for the observed effect,” one of which was that “a more confined distribution may increase the impact of stochastic events that increase mortality rates of delta smelt . . . includ[ing] . . . anthropogenic effects such as contaminants.” BiOp at 234 (citing Sommer et al. 2007). One other harmful effect of Project operations, the FWS notes, comes from the flow created by Project pumping: Projectrelated flow “increase[s] exposure to many pesticides [during spawning].” BiOp at 153. This is because flows can “mobilize contaminants.” BiOp at 240. The district court concluded that “[i]t is not clear how the BiOp or any other document in the record links the impacts of contaminants to Project Operations.” San Luis & DeltaMendota, 760 F. Supp. 2d at 942. This criticism is not wellfounded. Although the FWS recognized that “contaminant loading and its ecosystem effects within the Delta are not well understood,” BiOp at 186, the fact that science must advance further before the complicated ecosystem interactions in the Bay-Delta are fully understood does not necessarily mean that the FWS failed to rely on the best available science, or that it arbitrarily and capriciously concluded that there was a reasonable certainty that Project operations will indirectly affect smelt through water contamination. Appellees would presumably have us hold that it is impossible for an agency to simultaneously recognize that some characteristics of an indirect effect “are not well understood” or are “highly uncertain,” and that it is reasonably certain that those indirect effects are harmful and will result from the actions at issue. We decline to do so. We are confident that if we returned the BiOp to the agency, we could help the agency improve it by “point[ing] out errors and missing information” and SAN LUIS V. JEWELL 103 “insist[ing] on additional detail.” Churchill Cnty. v. Norton, 276 F.3d 1060, 1081 (9th Cir. 2001). But “[t]hat is not our role, of course.” Id. Instead, we hold that the BiOp has sufficiently explained the harmful relation between Project operations, contaminants, and delta smelt such that the FWS did not arbitrarily and capriciously conclude to a reasonable certainty that Project operations contribute to harmful contaminant-related indirect effects. 3. Project operations indirectly affect the smelt through the “other stressors” of predation, macrophytes, and microcystis As part of its analysis of the impacts of Project operations on the smelt, the BiOp discusses the effect of “other stressors”: “the multitude of factors that affect delta smelt population dynamics including predation, . . . aquatic macrophytes, and microcystis.” BiOp at 202. Because “[t]he extent to which these factors adversely affect delta smelt is related to hydrodynamic conditions in the Delta, which in turn are controlled to a large extent by CVP and SWP operations,” BiOp at 202, the BiOp’s analysis considers these “other stressors” when determining “the effects of proposed CVP/SWP operations on delta smelt,” BiOp at 203. We hold first that the BiOp’s conclusion that “hydrodynamic conditions driven or influenced by CVP/SWP operations . . . influence the dynamics of delta smelt interaction with these other stressors” was sufficiently supported in the record. BiOp at 202. The BiOp explicitly relied on outside scientific studies in concluding that Project operations “affect[] or control[]” some of these other stressors impacting delta smelt abundance. BiOp at 203. Second, having determined that Project operations likely affect the impact of “other stressors” 104 SAN LUIS V. JEWELL on the smelt, the BiOp considers the harmful effects of these other stressors on the smelt population. The district court faulted the FWS for failing to consider “available information,” San Luis & Delta-Mendota, 760 F. Supp. 2d at 934–35, or “make a rational connection between the facts in the record and its conclusions,” id. at 936. We disagree, and find the BiOp sufficiently thorough and hold that the FWS did not arbitrarily and capriciously reach this conclusion.
One stressor to the smelt is predation. As the BiOp acknowledged, there is much here that is unknown. It is known that in the 1960s, when the delta smelt were more plentiful, they were prey for striped bass, black crappie, and white catfish. BiOp at 183. The BiOp observed that “[i]t is unknown whether incidental predation by striped bass (and other lesser predators) represents a substantial source of mortality for delta smelt,” in part because the scarcity of smelt means that smelt have recently gone undetected in recent studies of predator stomach contents. BiOp at 183. The BiOp speculated that “[d]elta smelt may experience high predation mortality around water diversion where smelt are entrained and predators aggregate,” citing to an outside study showing that smelt eggs and larvae were prey for inland silversides. BiOp at 183. The BiOp also recognized the risks presented from other potential predators of smelt eggs and SAN LUIS V. JEWELL 105 larvae in such areas, including yellowfin goby, centrarchids, and Chinook salmon.41 BiOp at 183. After concluding that predation poses a harmful indirect effect to delta smelt population viability, the BiOp discusses the relation between Project operations and smelt predation. By shifting X2 further upstream, Project operations move smelt habitat to include more of these littoral areas (areas of the Bay-Delta close to shore), thereby increasing smelt exposure to predators. BiOp at 153. As the location of X2 shifts towards new Bay-Delta regions that are rife with predators the smelt follow. Moreover, the BiOp notes that “[t]he Delta-wide increase in water transparency may have intensified predation pressures on delta smelt,” citing the “[w]ide documenta[tion],” including several specific studies, indicating that water clarity significantly influences predation of pelagic fishes, including many smelt species. BiOp at 183. The district court found this analysis lacking, specifically citing the FWS’s failure to explain whether striped bass predation on the smelt “should be considered significant.” San Luis & Delta-Mendota, 760 F. Supp. 2d at 934. Here, the district court was very specific: It faulted the BiOp for failing to “include any estimates of the effect of predation on the delta smelt population” when “[s]uch information was available” and was “decidedly contrary to BiOp findings.” Id. In support, the district court referenced a 1999 California 41 The BiOp considered whether delta smelt are affected by competition from other fish, and cited one study suggesting that they were. The BiOp did not rely on competition as a stressor, however, because “there is no empirical evidence to support the conclusion that competition between these species is a factor that influences the abundance of delta smelt in the wild.” BiOp at 183. 106 SAN LUIS V. JEWELL Department of Fish & Game report submitted to the FWS as part of an incidental take permit. Id. (citing California Dep’t of Fish and Game, Conservation Plan for The California Department of Fish and Game Striped Bass Management Program (November 12, 1999)). We are not sure what the Fish & Game report adds, and we cannot see that the report is “decidedly contrary” to the FWS’s conclusions. The Fish & Game report stated that “the best available information yields imprecise, loosely constrained estimates of striped bass predation on delta smelt.” It said that there was considerable overlap between the delta smelt and striped bass, but that “striped bass rarely ate delta smelt,” likely because “delta smelt are surface oriented while striped bass tend to forage near the bottom.” Fish & Game estimated that striped bass were responsible for “an estimated annual consumption of about 5.3% of the delta smelt population.” We do not see the conflict between the Fish & Game report and the BiOp. The BiOp concluded that “[i]t is unknown whether incidental predation by striped bass . . . represents a substantial source of mortality for delta smelt.” BiOp at 183. The Fish & Game report is consistent with the FWS’s conclusion. If anything, the Fish & Game report might suggest that further study is required to see if the striped bass is a substantial source of mortality for the smelt. Although the BiOp cited post-1999 studies showing that the striped bass was not a significant predator of the delta smelt, if Fish & Game is correct (and current) in its estimates, a predator responsible for a 5.3 percent mortality rate of an endangered species might be significant. In any event, we fail to see its significance with respect to the conclusions of the BiOp. The BiOp concluded that predators—primarily fish other than the striped bass—were a potential threat to the delta smelt. More SAN LUIS V. JEWELL 107 broadly, we decline to review with a fine-toothed comb the studies on which the FWS relied in reaching its conclusions.
Another stressor noted in the BiOp is aquatic macrophytes. Macrophytes—aquatic plants that grow in or near water—have extensively colonized the interior Delta over the past two decades. BiOp at 182. As the BiOp notes, research suggests that these macrophytes have “altered fish community dynamics in the Delta, including increasing habitat for centrarchid fishes including largemouth bass, reducing habitat for native fishes, and supporting a food web pathway for centrarchids and other littoral fishes.” BiOp at 182 (citations to scientific studies omitted). These effects impact smelt both directly and indirectly. Submerged aquatic vegetation can overwhelm littoral habitats, such as inter-tidal shoals and beaches, that serve as delta smelt spawning locations, thereby rendering them unsuitable for spawning. BiOp at 182. Moreover, macrophytes trap suspended sediment and therefore reduce water turbidity, which has contributed to a decrease in both juvenile and adult smelt habitat while increasing the available habitat for fish that prey on smelt. BiOp at 182–83 (citing Feyrer et al. 2007 and Nobriga et al. 2008 in support). This decreased turbidity also may facilitate the predation of delta smelt while hampering the smelt’s own feeding, thereby further harming smelt population viability. BiOp at 183. As the BiOp notes, hydrologic conditions and water temperature play a significant role in macrophyte colonization of the Delta. BiOp at 182. The FWS concludes that it is “likely” that Project operations’ impact on Bay-Delta hydrologic conditions and reduction of seasonal flushing 108 SAN LUIS V. JEWELL flows exacerbate the spread of macrophytes in the Bay-Delta. BiOp at 277. Flushing flows are known to lead to “abrupt changes in flow and turbidity.” BiOp at 146. Indeed, the FWS found that “[a]vailable information is inconclusive regarding the extent, magnitude and pathways by which delta smelt may be affected by these stressors independent of CVP/SWP operations.” BiOp at 277. The district court was also not persuaded by this analysis, finding that “[a]lthough a connection [between Project operations and macrophytes] may exist, the record does not reflect any discussion, nor have the parties pointed to any study, connecting ‘seasonal flushing flows . . . the natural frequency of upstream and downstream movement of the LSZ, and lengthen[ed] upstream shifts of the LSZ’ to the presence of any aquatic macrophyte.” San Luis & DeltaMendota, 760 F. Supp. 2d at 935–36. Again, we disagree. The BiOp set forth several plausible explanations for how Project operations will increase the detrimental impact of macrophytes on delta smelt viability, and cited studies in support. See BiOp at 146, 182–83, 277. That the BiOp did not, as the district court requests, point to a study directly addressing the Project’s effect on Bay-Delta macrophytes does not render the FWS’s conclusions unreasonable or unsupported: The FWS has drawn rational conclusions from the best available science, and, consequently, we hold that the BiOp’s determination that it is reasonably certain that macrophytes will indirectly affect delta smelt is not arbitrary and capricious. It is not our job to task the FWS with filling the gaps in the scientific evidence. We must respect the agency’s judgment even “in the face of uncertainty.” Ariz. Cattle Growers Ass’n v. Salazar, 606 F.3d 1160, 1164 (9th Cir. 2010). SAN LUIS V. JEWELL 109
Microcystis aeruginosa is a cyanobacterium that produces toxins throughout its life cycle, with toxin concentrations sharply increasing when the bacteria population dies, usually in September or October. BiOp at 372. These high toxin levels present a threat to the delta smelt and, as the BiOp recognizes, high microcystis toxin levels have been associated with low delta smelt abundances. BiOp at 372. Microcystis can directly “pose animal and human health risks if contacted or ingested directly,” although it does not appear that current concentrations are sufficiently severe to threaten smelt. BiOp at 186. Rather, microcystis’s primary threat to the smelt is indirect, as “it appears that M. aeruginosa is toxic to copepods that delta smelt eat.” BiOp at 186 (citing an outside scientific study). There is also concern that microcystis “could out-compete diatoms[, a rich food source for zooplankton,] for light and nutrients.” BiOp at 186. As the BiOp notes, however, more studies are needed, and, in fact, “are underway to determine if zooplankton production is compromised during M. aeruginosa blooms to an extent that is likely to adversely affect delta smelt.” BiOp at 186. As the BiOp also discusses, CVP/SWP operations are likely to increase the harmful impact of microcystis on delta smelt because “[l]ow flow conditions are among the factors associated with Microcystis blooms.” BiOp at 372. By reducing flows, Project operations would cause “larval and juvenile delta smelt . . . [to] remain in the Central and South Delta, where they could . . . succumb to predation or microcystis blooms.” BiOp at 224. Overall, Microcystis “reduce[s] habitat suitability.” BiOp at 373. 110 SAN LUIS V. JEWELL The district court found that the BiOp “makes no connection whatsoever between microcystis . . . and continued CVP and SWP operation” and that “[g]iven that the impacts of regulating Project Operations are so consequential, such unsupported attributions (a result in search of a rationale) are unconscionable.” San Luis & Delta-Mendota, 760 F. Supp. 2d at 936. Again, we disagree. The FWS has proposed several plausible evidence-based hypotheses explaining the harmful Project-related impact of microcystis on delta smelt. The BiOp candidly acknowledges that additional studies in this area are underway. That CVP/SWP operations are not the only dynamic force acting on the Bay-Delta does not render the BiOp’s recognition of the inherent uncertainty associated with the highly interdependent ecosystem unreasonable. We should not deter agencies from recognizing the limitations of either science or their own knowledge. In this instance, the evidence linking Project operations, Bay-Delta hydrologic conditions, and microcystis harms is sufficient that we hold that the FWS’s microcystis conclusions were not arbitrary and capricious. E. The FWS Is Not Required to Support the “Non-Jeopardy” Elements of its RPA When the Secretary determines that an agency action will cause jeopardy to, or an adverse habitat modification of, an endangered or threatened species, the Secretary “shall suggest those reasonable and prudent alternatives which he believes would not [jeopardize the species or adversely modify its habitat] and can be taken by the Federal agency or applicant in implementing the agency action.” 16 U.S.C. § 1536(b)(3)(A); see also 50 C.F.R. §§ 402.14(h)(3), 402.14(g)(5). The FWS’s regulations further explain its duty SAN LUIS V. JEWELL 111 under the ESA. The regulations define “reasonable and prudent alternatives”—RPAs—as alternative actions identified during formal consultation [1] that can be implemented in a manner consistent with the intended purpose of the action, [2] that can be implemented consistent with the scope of the Federal agency’s legal authority and jurisdiction, [3] that is economically and technologically feasible, and [4] that the Director believes would avoid the likelihood of jeopardizing the continued existence of listed species or resulting in the destruction or adverse modification of critical habitat. 50 C.F.R. § 402.02. Element [4] in § 402.02 is commonly referred to as the “jeopardy” factor; elements[1] through [3] are referred to as the “non-jeopardy” factors. The FWS’s Consultation Handbook explains further: “If the services conclude that certain alternatives are available that would avoid jeopardy and adverse modification, but such alternatives fail to meet one of the other three elements in the definition of ‘reasonable and prudent alternative,’ the Services should document the alternative in the biological opinion to show it was considered during the formal consultation process.” U.S. Fish & Wildlife Serv. & Nat’l Marine Fisheries Serv., ESA at 4–41 (March 1998), Section 7 Consultation Handbook, available at http://www.fws.gov/ endangered/esa-library/pdf/CH4.pdf (last visited July 27, 2013) (second emphasis added). Thus, according to the Consultation Handbook, if a draft alternative fails to meet one of the non-jeopardy “elements” of a valid RPA, the Service should provide documentation to show that it considered 112 SAN LUIS V. JEWELL alternatives during consultation. Id. We have previously afforded Skidmore deference to the FWS’s Consultation Handbook. See Ariz. Cattle Growers’ Ass’n, 606 F.3d at 1165 (concluding that “[t]he definition in the handbook appears to be the result of the agency's considered judgment and . . . we are persuaded [that it is a reasonable one entitled to deference]”); see also Skidmore v. Swift & Co., 323 U.S. 134 (1944). Referring to the non-jeopardy factors, the district court found that the FWS “has articulated absolutely no connection between the facts in the record and the required conclusion that the RPA is (1) consistent with the purpose of the underlying action; (2) consistent with the action agency’s authority; and (3) economically and technologically feasible.” San Luis & Delta-Mendota, 760 F. Supp. 2d at 956–57. The court continued: the APA requires, and the public is entitled under the law to receive, some exposition in the record of why the agency concluded (if it did so at all) that all four regulatory requirements for a valid RPA were satisfied. The RPA Actions manifestly interdict the water supply for domestic human consumption and agricultural use for over twenty million people who depend on the Projects for their water supply. “Trust us” is not acceptable. FWS has shown no inclination to fully and honestly address water supply needs beyond the species despite the fact that its own regulation requires such consideration. SAN LUIS V. JEWELL 113 San Luis & Delta-Mendota, 760 F. Supp. 2d at 957. Because the FWS had failed to explain why it chose its RPAs, “to the exclusion of implementing less harmful alternatives,” the district court remanded to the FWS. Id. Put more simply, the district court found that both the FWS’s regulation and the APA required the FWS to engage in a record exposition of the non-jeopardy factors, and that the FWS did not do so. We disagree both with the district court’s legal analysis and with its reading of the record. First, contrary to the district court’s conclusion, the FWS’s “own regulation” does not require the FWS to address the non-jeopardy factors. San Luis & Delta-Mendota, 760 F. Supp. 2d at 957. Nothing in § 402.02 obligates the FWS to address the non-jeopardy factors when it proposes RPAs. Section 402.02 is a definitional section; it is defining what constitutes an RPA, not setting out hoops that the FWS must jump through. See 50 C.F.R. § 402.02; see also id. at § 402.14(g)(5) (the FWS shall “discuss [with an agency] . . . the availability of reasonable and prudent alternatives”); 302.02(h)(3) (“A ‘jeopardy’ biological opinion shall include reasonable and prudent alternatives if any.”). Moreover, the Consultation Handbook implies that no such discussion is necessary. As the Handbook notes: [Although] it is imperative that the opinion contain a thorough explanation of how each component of the [reasonable and prudent] alternative is essential to avoid jeopardy and/or adverse modification[,] . . . [i]f the Services conclude that certain alternatives are available that would avoid jeopardy and adverse modification, but such alternatives 114 SAN LUIS V. JEWELL fail to meet one of the other three elements in the definition of ‘reasonable and prudent alternative,’ the Services should document the alternative in the biological opinion to show it was considered during the formal consultation process. Section 7 Consultation Handbook, available at http://www.fws.gov/endangered/esa-library/pdf/CH4.pdf (last visited July 1, 2013); see also 50 C.F.R. § 402.14(h)(3) (“If the Service is unable to develop such [reasonable and prudent] alternatives, it [must] indicate that to the best of its knowledge there are no reasonable and prudent alternatives.”). In other words, a “thorough” documentation of jeopardy/adverse modification in the BiOp is always required, whereas documentation of the non-jeopardy factors is only required when the RPA fails to meet a non-jeopardy factor. We fail to see anywhere that the FWS has required itself to provide an explanation of the non-jeopardy factors when it lays out an RPA. We may not “impose on the agency [our] own notion of which procedures are ‘best’ or most likely to further some vague, undefined public good. Nor may we impose procedural requirements [not] explicitly enumerated in the pertinent statutes.” McNair, 537 F.3d at 993 (internal quotation marks and citations omitted).42 42 We note that the Fourth Circuit recently remanded a BiOp to the FWS for failure to evaluate an RPA for its economic and technological feasibility. Dow AgroSciences, 707 F.3d at 474–75. We do not read Dow to require the FWS to address economic and technological feasibility as a procedural matter. As we read Dow, the court was concerned that the FWS had imposed an especially onerous requirement without any thought for whether it was feasible. Id. at 475 (RPA would prohibit pesticide SAN LUIS V. JEWELL 115 Second, the APA does not, as the district court held, require the FWS to address the non-jeopardy factors in this case. See San Luis & Delta-Mendota, 760 F. Supp. 2d at 957. Under the APA, the Supreme Court has held that agency decisions that “entirely fail[] to consider an important aspect of the problem” are arbitrary and capricious. Motor Vehicle Mfrs. Ass’n of U.S., Inc. v. State Farm Mut. Auto Ins. Co., 463 U.S. 29, 43 (1983). We have held that whether an RPA will prevent jeopardy or adverse modification of critical habitat is “an important aspect of the problem.” See, e.g., Wild Fish Conservancy v. Salazar, 628 F.3d 513, 522–23 (9th Cir. 2010) (finding a BiOp that failed to explain how the RPA avoided jeopardy arbitrary and capricious); Pacific Coast Fed’n of Fishermen’s Ass’ns v. U.S. Bureau of Reclamation, 426 F.3d 1082, 1091 (9th Cir. 2005) (same). But the jeopardy factor in the RPA is independently demanded by the ESA itself. Section 1536(a)(2) requires that each federal agency shall “insure that any action . . . is not likely to jeopardize” the species or its habitat. 16 U.S.C. § 1536(a)(2). This includes the FWS, which must warrant that its RPA “would not violate [§ 1536(a)(2)].” 16 U.S.C. § 1536(b)(3)(a). That is, the FWS, in the course of proposing an RPA, must insure that the RPA does not jeopardize the species or its habitat. We can find no similar requirement in the ESA that the FWS address the remaining three nonjeopardy factors. If the ESA does not require it, we are extremely reluctant to read such a requirement into the APA. applications “within 500 feet (for ground applications) and 1,000 feet (for aerial applications) of any waterway that is connected, directly or indirectly, at any time of the year, to any water body in which salmonids might be found at some point.” (emphases in original)). 116 SAN LUIS V. JEWELL Moreover, we are persuaded that the district court misread what the economic feasibility factor addresses. The court faulted the FWS for not accounting for the cost of “interdict[ing] the water supply for domestic human consumption and agricultural use for over twenty million people who depend on the Projects for their water supply.” San Luis & Delta-Mendota, 760 F. Supp. 2d at 957. This misreads the ESA and its implementing regulations. Section 402.02 is only concerned with the economic and technological feasibility of the RPA. That is, the FWS must consider whether its proposed alternative is financially and technologically possible. Those two considerations— economics and technology—are constraints on what measures the FWS can recommend to the agency as an alternative to ceasing the activity entirely. To put it into perspective in this case: Reclamation has consulted with the FWS because it has legitimate concerns whether its continued CVP activities may jeopardize the smelt or its habitat. When the FWS concludes that Reclamation’s continued activities will jeopardize the smelt then, presumptively, Reclamation may not take or continue such activities. See Nat’l Assn of Home Builders v. Defenders of Wildlife, 551 U.S. 644, 652 (2007) (“Following the issuance of a ‘jeopardy’ opinion, the agency must either terminate the action, implement the proposed alternative, or seek an exemption from the CabinetLevel Endangered Species Committee . . . .”). In this case, of course, terminating Reclamations’ CVP-related activities is unthinkable. The whole point of the “reasonable and prudent alternative” is for the FWS to suggest what Reclamation can do to avoid such a result. The regulation identifies “economic and technological feasibility” as factors because these go to whether the RPA “can be taken by the Federal agency . . in implementing the agency action,” 16 U.S.C. § 1536(b)(3)(A) (emphasis added), not to whether restricting SAN LUIS V. JEWELL 117 CVP activities will affect its consumers.43 The “economic and technological feasibility” factor does not address the downstream economic impacts of Reclamation being unable to continue its CVP operations as it has done in the past. As important and consequential as the question is, the FWS is not responsible for balancing the life of the delta smelt against the impact of restrictions on CVP/SWP operations. That balance has already been struck by Congress in the ESA and the Central Valley Project Improvement Act. See CVPIA § 3406(b), Pub. L. No. 102-575, 106 Stat. 4600, 4714 (stating that the Secretary of the Interior is to “operate the Central Valley Project to meet all obligations under State and Federal law, including but not limited to the Federal Endangered Species Act, 16 U.S.C. § 1531 et seq”); Tenn. Valley Auth., 437 U.S. at 185 (holding that the ESA reflects “a conscious decision by Congress to give endangered species priority over the ‘primary missions’ of federal agencies”). Accordingly, the FWS’s duty is to opine on the viability of the smelt and “to halt and reverse the trend toward species extinction, whatever the cost.” Id. at 184 (emphasis added). Even if the APA did require the FWS to consider the nonjeopardy factors, the record shows that the FWS has sufficiently considered them. See Motor Vehicle Mfrs. Ass’n of U.S., Inc, 463 U.S. at 43 (holding that insufficient consideration for purposes of APA arbitrary and capricious review is an “entire[] fail[ure] to consider”). Although the FWS’s consideration of the non-jeopardy factors could certainly have been even more exhaustive, or stated more expressly, its determination that the RPA satisfied the non- 43 Neither the parties nor the district court argue that the RPAs themselves (and their proposed Actions) are not economically and technologically feasible. 118 SAN LUIS V. JEWELL jeopardy factors “may be reasonably discerned” from the record, and therefore should be upheld. Id. (holding that even “a decision of less than ideal clarity” should be upheld in such circumstances). Application of the non-jeopardy factors in this case is really quite straightforward. See 50 C.F.R. § 402.02. First, the record shows that the RPA is consistent with the purpose of the underlying action. The document that was prepared as a basis for consultation, Reclamation’s biological assessment (BA), identified the purpose of this action to be “operat[ing] the [Projects] to divert, store, redivert, and convey CVP and SWP . . . water consistent with applicable law.” The RPAs—which largely deal with regulating the water that CVP/SWP export from the DeltaBay—do not require any major changes in the way Reclamation runs its operations. Second, and similarly, the record indicates that the RPA can be implemented “consistent with the scope of the Federal agency’s legal authority.” Both the BA and the BiOp discuss the extent of Reclamation’s authority. See, e.g., BiOp at 21–25 (Reclamation’s obligations under its Coordinated Operations Agreement with DWR). Finally, there is support in the record for the FWS’s conclusion that the RPA is both technologically and economically feasible. We think this is nearly self-evident. The RPA closely resembles measures in the interim remedial order, the feasibility of which was proven in its mid-December 2007 through December 2008 implementation. BiOp at 327–28. Additionally, RPA incorporates feasibility-related comments that were made on a draft RPA from Reclamation and DWR. Again, the RPAs propose regulatory changes in what Reclamation does on a day-to-day basis, but the RPAs do not require major changes affecting Reclamation’s ability—financially or technologically—to comply with the RPAs. SAN LUIS V. JEWELL 119 In sum, we disagree with the district court’s determination that the FWS’s own regulation and the APA require the FWS to explain that the RPA satisfies § 402.02 non-jeopardy factors. Alternatively, we hold that the FWS’s consideration of these factors may be reasonably discerned from the record to satisfy any explanation requirements.44