Life in a Fishbowl: Prospects for the Endangered Devils Hole Pupfish

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Water Resources Research

RESEARCH ARTICLE Life in a fishbowl: Prospects for the endangered Devils Hole 10.1002/2014WR015511 pupfish (Cyprinodon diabolis) in a changing climate

Special Section: Mark B. Hausner1,2,3, Kevin P. Wilson4, D. Bailey Gaines4, Francisco Suarez 2, G. Gary Scoppettone5, Eco-hydrology of Semiarid and Scott W. Tyler1 Environments: Confronting Mathematical Models with 1Department of Geological Sciences and Engineering, University of Nevada, Reno, Nevada, USA, 2Departamento de Ecosystem Complexity Ingenierıa Hidraulica y Ambiental, Pontificia Universidad Catolica de Chile, Santiago, Chile, 3Division of Hydrologic Sciences, Desert Research Institute, Las Vegas, Nevada, USA, 4Pahrump Field Office, Death Valley National Park, Pahrump, 5 Key Points: Nevada, USA, Western Fisheries Research Center, United States Geological Survey, Reno, Nevada, USA Numerical simulations predict habitat temperatures under climate scenarios Abstract The Devils Hole pupfish (Cyprinodon diabolis) is a federally listed endangered species living Combining CFD with ecological solely within the confines of Devils Hole, a geothermal pool ecosystem in the Mojave Desert of the Ameri- modeling quantifies impacts of climate change can Southwest. This unique species has suffered a significant, yet unexplained, population decline in the Devils Hole is an indicator of changes past two decades, with a record low survey of 35 individuals in early 2013. The species survives on a highly expected in similar arid ecosystems variable seasonal input of nutrients and has evolved in a thermal regime lethal to other pupfish species. The short lifespan of the species (approximately 1 year) makes annual recruitment in Devils Hole critical to the Correspondence to: persistence of the species, and elevated temperatures on the shallow shelf that comprises the optimal S. W. Tyler, [email protected] spawning habitat in the ecosystem can significantly reduce egg viability and increase larval mortality. Here we combine computational fluid dynamic modeling and ecological analysis to investigate the timing of Citation: thresholds in the seasonal cycles of food supply and temperature. Numerical results indicate a warming cli- Hausner, M. B., K. P. Wilson, D. B. mate most impacts the heat loss from the water column, resulting in warming temperatures and reduced Gaines, F. Suarez, G. Gary Scoppettone, buoyancy-driven circulation. Observed climate change is shown to have already warmed the shallow shelf, and S. W. Tyler (2014), Life in a fishbowl: Prospects for the and climate change by 2050 is shown to shorten the window of optimum conditions for recruitment by as endangered Devils Hole pupfish much as 2 weeks. While there are many possible reasons for the precipitous decline of this species, the (Cyprinodon diabolis) in a changing changing climate of the Mojave region is shown to produce thermal and nutrient conditions likely to reduce climate, Water Resour. Res., 50, 7020– 7034, doi:10.1002/2014WR015511. the success of annual recruitment of young C. diabolis in the future, leading to continued threats to the survival of this unique and enigmatic species. Received 25 FEB 2014 Accepted 7 AUG 2014 Accepted article online 11 AUG 2014 Published online 27 AUG 2014 1. Introduction Global climate change is expected to cause major shifts in distributions of flora and fauna [Warren et al., 2013]. However, endemic species in very restricted and specialized environments are unable to move to more favorable climatic conditions and must therefore adapt or die in their respective restricted habitats. One such habitat is Devils Hole (36.42N, 115.28W), a groundwater-fed pool ecosystem in the Mojave Desert of the American Southwest that is home to the only extant population of Devils Hole pupfish (Cypri- nodon diabolis)[Wales, 1930]. C. diabolis is a federally listed endangered species [U.S. Department of the Inte- rior, 1973] that is believed to occupy the smallest known habitat of any vertebrate [Moyle, 2002] and has evolved to endure elevated water temperatures, depressed dissolved oxygen (DO), and significant climate changes. This enigmatic species and its ecosystem has been at the center of a wide range of debates, from questions on the how it came to occupy Devils Hole (there is no known surface water connection, nor has there likely been one since the fish first populated Devils Hole) [Szabo et al., 1994; Riggs and Deacon, 2004], the disposition of federal verses state water rights [Deacon and Williams, 1991; Riggs and Deacon, 2004], the drivers of glacial/interglacial transition [Winograd et al., 1988], and finally, questions regarding the role of genetics in conservation biology [Martin et al., 2012]. Despite extensive conservation efforts in Devils Hole, C. diabolis now appears to be facing its second threat of extinction in less than 50 years, without a clear cause for the population decline. The focus of over 40 years of management and conservation efforts, C. diabolis was first threatened with extinction in the 1970s when groundwater withdrawals reduced the water level on the 14 m2 shallow shelf [Deacon and Williams, 1991] that provides its primary spawning habitat [James, 1969]. After litigation

600 Spring Count reaching as far as the U.S. Autumn Count 500 Recovery Supreme Court halted nearby Stable Decline groundwater pumping (U.S. v. 400 Cappaert 1974; Cappaert v. U.S. 300 1976) [Deacon and Williams, 1991], the population recov- 200 Relative Abundance ered (Figure 1) as the water 100 table rose [Andersen and Dea-

0 con, 2001]. Since 1995, how- 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 ever, the C. diabolis population Figure 1. Population surveys of C. diabolis in Devils Hole, showing both the earlier recovery has, for reasons not yet under- (1970–1980) and the recent (post-1995) unexplained decline of the population. stood, been in a second decline [Riggs and Deacon, 2004]. By 2013, the annual population count had reached its lowest level, with only 35 individuals recorded. A number of different hypotheses have been advanced to explain the recent decline in the C. diabolis population, including inbreeding depression [Wilcox, 2001], shifts in the microbial and algal community [Riggs and Deacon, 2004; Bernot and Wilson,2012],orsedimentdynamics[Lyons,2005],and the loss of a key prey species from the primary feeding habitat [Herbst and Blinn, 2003]. In this study, we consider whether ongoing climate change may have negatively affected the annual recruitment of C. diabolis. The species’ 10–14 month lifespan [James, 1969] makes successful annual recruitment critical to the survival of the species, and this recruitment requires (among other factors) the coincidence of water temperatures suitable for successful egg hatching [Shrode, 1975; Shrode and Gerking, 1977] and sufficient food for newly hatched larvae. In spite of its ability to survive in a very harsh environment, past efforts at breeding C. diabolis in captivity or maintenance of satellite populations in refuges have been largely unsuccessful. The reproductive life stages of Cyprinodon spp., especially developing embryos [Shrode, 1975], are strongly influenced by water temperatures [Shrode and Gerking, 1977]. For closely related Cyprinodon nevadensis nevadensis, the optimal range of water temperatures for embryo development is between 24 and 30C, and hatching success drops quickly in warmer environments—in C. n. nevadensis, hatching success drops from 80% to approximately 10% when incubation temperatures increase from 30 to 32C[Shrode and Gerking, 1977]. A successful hatch, though, does not guarantee survival. Although the rate of embryo development is relatively independent of temperature, warmer eggs hatch earlier and are often underdeveloped [Shrode, 1975] and thus unlikely to survive to reproduce. Embryos spawned by Cyprinodon spp. acclimated to a fluctuating temperature (like that seen on the shallow shelf of Devils Hole) had greater success at higher temperature ranges than embryos spawned under constant temperatures [Shrode and Gerking, 1977]. Adult C. diabolis, though, maintain constant temperatures by using the constant-temperature deep pool of Devils Hole to escape daily peak temperatures observed on the shallow shelf [Baugh and Deacon, 1983a]. Historically, the C. diabolis population has cycled annually, with spring lows and autumn highs [Riggs and Deacon, 2004]. Spring and autumn surveys in 2013 counted 35 and 65 individuals, respectively, whereas pre-1995 surveys averaged 200 (spring) and 400 (autumn) individuals (Figure 1). Although C. diabolis spawn year-round [Miller, 1961; La Rivers, 1962], the majority of recruitment typically occurs in the springtime [Hausner et al., 2013], when a seasonal shift from a predominantly allochthonous winter to a predominantly autochthonous summer food web begins [Wilson and Blinn, 2007]. In autumn, simultaneous decreases in solar-driven primary productivity and allochthonous contributions lead to a food-limited winter and annual mortality [Minckley and Deacon, 1975]. The bulk of annual recruitment occurs during the period each spring after the seasonal increase in food availability begins, but before water temperatures reach their seasonal peaks [Hausner et al., 2013]. The Mojave Desert’s increasing temperature is suspected to have negatively influenced recruitment success, contributing to the present population decline [Hausner et al., 2013]. Devils Hole is a flooded fracture in a carbonate aquifer and comprises a shallow shelf, used daily by C. diabolis for foraging and spawning, and a groundwater-fed deep pool. With no surface inlet or outlet, water temperatures on the shallow shelf are controlled by its connection to the constant-temperature groundwater, meteorological conditions, and seasonally variable direct solar radiation [Hausner et al., 2013]. The canyon

surrounding Devils Hole limits direct radiation to less than 4.5 h d21 during the peak of summer. The constant 2.5–3 mg L21 DO concentrations and 33.5C temperature in the deep pool are near the edge of the survivable range for Cyprinodon spp.[Shrode, 1975; Shrode and Gerking, 1977; Riggs and Deacon, 2004]. The inability to migrate beyond its restricted habitat, small population, and proximity to critical thresholds [Riggs and Deacon, 2004] combine to make C. diabolis extremely sensitive to changes in its habitat [Hausner et al., 2013]. Cyprinodon diabolis, exposed to temperatures near its thermal maximum before recent climate change, is now subject to increasing water temperatures which may be reducing successful recruitment and ultimately leading to population decline. Here we combine computational ﬂuid dynamic (CFD) modeling and ecological analysis to investigate the effect of climate change on the timing of thresholds in the seasonal cycles of food supply and temperature in the ecosystem. Speciﬁcally, we examine the impacts of predicted warming air temperatures through this century on the shallow shelf temperatures during critical times of egg development and larval recruitment.

2. Methods This work integrates climate projections, CFD modeling, and ecological analysis to examine the effects of climate change on the Devils Hole ecosystem. Historical, present, and projected climate scenarios were identi- ﬁed for the Amargosa Desert; these scenarios were used to drive a thermal model of the shallow shelf [Hausner et al., 2013] and to estimate the availability of food in the ecosystem. Thresholds in the seasonal variations of simulated water temperatures and available food were then used to deﬁne an annual period of optimal recruitment conditions. The duration of this period was used to consider climate-driven changes in the suitability of the Devils Hole ecosystem for C. diabolis.

2.1. Site Description and Local Climate Records The shallow shelf of Devils Hole is a boulder perched between the two walls of the fracture’s aperture. Water depth on the shelf ranges from a few cm to more than 80 cm, with an average depth of 35 cm. The water in Devils Hole is subject to earth tides, which cause a semidiel variation in water depth that is typically 2–3 cm [Dudley and Larson, 1976], but occasionally reaches up to 12 cm [Riggs and Deacon, 2004]. Along the eastern edge of the shelf, a space between the boulder and the fracture wall allows hydraulic communi- cation between the water column above the shelf and the constant-temperature reservoir of flowing groundwater below it. Between late spring and autumn, the shelf’s substrate is typically covered with algae (Spirogyra, Oscillatoria, and Plectonema spp.), but algae coverage is greatly reduced during the winter and the substrate is primarily bare sand, gravel, and rock. Both observed and projected climatological data were used to drive a physical model of the ecosystem. Average annual temperature observations in southern Nevada were compiled from two different sources: spatially distributed temperatures from the PRISM climate group (PRISM Climate Group, Oregon State Uni- versity, http:/prism.oregonstate.edu, created 4 February 2004) and local data recorded by the National Atmospheric and Oceanic Administration (NOAA) cooperative observer program. Based on the parameter- elevation relationships on independent slopes model (PRISM) [Daly et al., 2008], monthly temperatures from 1960 to 2012 were obtained for the 16 km2 cell containing Devils Hole, and 30 year normal temperatures for the periods 1970–2000 and 1980–2010 were obtained for the 64 ha cell containing Devils Hole. Local temperature observations were obtained from the NOAA’s Cooperative Observer meteorological station at Amargosa Farms (36.57N, 116.46W) [NOAA Cooperative Observer station 210650, accessed 15 October 2012], approximately 20 km north of Devils Hole. Monthly temperatures were composited into annual mean values as the mean of the 12 months. Statistical correlations between time and temperature were performed by linear regression using the MATLAB function regress. Results reported include the number of samples (n), coefficient of determination (R2), and confidence level (p).

2.2. Simulating Different Climate Scenarios Simulations of present conditions were driven with meteorological data collected onsite by the National Park Service 2009–2012. Past and future simulations were run by applying an offset to the air temperatures in this meteorological data set. For past simulations, a single offset was applied to the air temperature based on the difference between present and historical monthly mean temperature. Temperature offsets for future simulations were determined with a hybrid delta approach based on the CMIP5 multimodel

ensemble output (Table 1) [Maurer et al., 2007; U.S. Bureau of Reclamation, 2013]. GCM results were downscaled to the 0.125 latitude 3 0.125 longitude grid cell containing Devils Hole, and the 10th percentile, median, and 90th percentile were determined for the monthly projections of the 234 models. For each of the three hybrid scenarios (10th, 50th, and 90th percentiles), monthly temperatures were determined for each simulated timeframe (2040–2049 and 2090–2099), and these monthly temperatures were used to establish the delta value for the timeframe and percentile temperature change (Table 2).

2.3. Computational Fluid Dynamic Modeling A meteorologically driven CFD model of the shallow shelf [Hausner et al., 2013] was used to simulate the response of the water column to changes in local air temperature. Created with FLUENT (Ansys Inc., USA), the model simulates two-dimensional convection in a cross section of the vertical east-west plane above the shallow shelf of Devils Hole. The model requires inputs of water depth, air temperature, relative humid- ity, direct solar radiation, and wind speed. The model was parameterized as described by Hausner et al. [2013], and 24 h long simulations were run using a 10 s time step. This parameterization, time step, and duration accurately represented observed temperatures under both summer and winter conditions at a range of water depths [Hausner et al., 2013]. Monthly simulations were run using meteorological data representing the average day of the given month. The depth of the water column on the shallow shelf of Devils Hole varies in both space (according to bathymetry) and time (due primarily to barometric pressure and earth tides) [Dudley and Larson, 1976]. The model was run with a depth of 0.35 m, representing the mean depth of water over the shelf in space and time. Simulations were run to represent historical conditions (1980–1989), present conditions (2000–2009), midrange projections (10th, 50th, and 90th percentiles of projected temperatures for 2040–2049), and long-term projections (10th, 50th, and 90th percentiles of projected temperatures for 2090–2099). Because the spatial variation of temperature on the shallow shelf is much less than the temporal variations [Hausner et al., 2013], the mean temperature of the entire water column was recorded every 150 s over the 24 h simulation, and the peak daily temperature was calculated as the maximum of those means during the 24 h simulation.

2.4. Food Web Projections Projections of monthly allochthonous contributions were based on data reported by Wilson and Blinn [2007] between 1999 and 2003. The reported mean monthly rate of allochthonous contributions (averaged over the 4 year period) was linearly correlated to the mean monthly temperature (again averaged over the 4 year period) recorded at the Amargosa Farms Cooperative Observer Meteorological station [NOAA Coop- erative Observer station 210650, accessed 15 October 2012] (R2 5 0.93, F 5 126, p < 1026,n5 12). The springtime allochthonous contributions to Devils Hole are primarily plant material and insect carcasses [Wil- son and Blinn, 2007], and both plants and insects exhibit phenological responses to seasonal variations in temperature [Cleland et al., 2007]. Climate change has been linked to earlier spring blooms and green-up [Parmesan and Yohe, 2003; Root et al., 2003], and seasonal development and reproductive activity in insects is often regulated by temperature [Wolda, 1988; Deutsch et al., 2008]. Past and future allochthonous contri-

butions were therefore simulated according to equation (1), in which Nalloch is the rate of allochthonous 21 input to the shallow shelf (kJ d ), Tm is the mean monthly temperature ( C), and the empirical constants a and b (0.66 kJ d21C21 and 5.91 kJ d21, respectively) are derived from the linear correlation

Nalloch5aTm1b: (1) Autochthonous production on the shallow shelf is driven by primary productivity (primarily ﬁlamentous cya- nobacteria and Spyrogyra spp.). Although many ecosystems depend on a climate-driven seasonal turnover to mobilize nutrients and drive primary productivity [O’Reilly et al., 2003], the deep pool of Devils Hole is strongly oligotrophic [Hausner et al., 2012] and light, rather than nutrients, is the limiting factor in autochthonous production [Wilson and Blinn, 2007]. Algae in resource-limited settings are less sensitive to temperature changes than those in resource-saturated settings [Raven and Geider, 1988; Winder and Sommer, 2012], and the light reaching Devils Hole is unlikely to change signiﬁcantly—projected increases in overall radiative forcing are due to net longwave radiation rather than photosynthetically available shortwave radiation. While we expect allochthonous contributions to respond to local temperature changes, the seasonal cycle of primary productivity within Devils Hole is expected to remain unchanged from the productivity reported by Wilson and Blinn [2007]. Total food availability is therefore projected as the sum of the

Table 1. Models Included in the Climate Projection Ensemblea Institution Model RCP Scenario(s) Commonwealth Scientiﬁc and Industrial access1-0.1 RCP4.5 Research Organization (CSIRO), RCP8.5 Australia, and Bureau of Meteorology access1–3.1 RCP4.5 (BOM), Australia RCP8.5 Beijing Climate Center, China bcc-csm1-1.1 RCP2.6 Meteorological Administration RCP4.5 RCP6.0 RCP8.5 bcc-csm1-1(m).1 RCP4.5 RCP8.5 College of Global Change and Earth bnu-esm.1 RCP2.6 System Science, Beijing Normal University RCP4.5 RCP8.5 Canadian Centre for Climate Modeling and Analysis canesm2.1 RCP2.6 RCP4.5 RCP8.5 canesm2.2 RCP2.6 RCP4.5 RCP8.5 canesm2.3 RCP2.6 RCP4.5 RCP8.5 canesm2.4 RCP2.6 RCP4.5 RCP8.5 canesm2.5 RCP2.6 RCP4.5 RCP8.5 National Center for Atmospheric Research (NCAR) ccsm4.1 RCP2.6 RCP4.5 RCP6.0 RCP8.5 ccsm4.2 RCP2.6 RCP4.5 RCP6.0 RCP8.5 ccsm4.3 RCP2.6 RCP4.5 RCP6.0 RCP8.5 ccsm4.4 RCP2.6 RCP4.5 RCP6.0 RCP8.5 ccsm4.5 RCP2.6 RCP4.5 RCP6.0 RCP8.5 National Science Foundation, cesm1-bgc-1 RCP4.5 Department of Energy, NCAR RCP8.5 cesm1-cam5-1 RCP2.6 RCP4.5 RCP6.0 RCP8.5 cesm1-cam5-2 RCP2.6 RCP4.5 RCP6.0 RCP8.5 cesm1-cam5-3 RCP2.6 RCP4.5 RCP6.0 RCP8.5 Centro Euro-Mediterraneo per I Cambiamenti Climatici cmcc-cm.1 RCP4.5 RCP8.5 cmcc-cm5.1 RCP4.5 RCP8.5 cmcc-cm5.1 RCP4.5 RCP8.5

Table 1. (continued) Institution Model RCP Scenario(s) cmcc-cm5.2 RCP8.5 cmcc-cm5.4 RCP8.5 cmcc-cm5.6 RCP8.5 cmcc-cm5.10 RCP8.5 CSIRO csiro-mk3–6-0.1 RCP2.6 RCP4.5 RCP6.0 RCP8.5 csiro-mk3–6-0.2 RCP2.6 RCP4.5 RCP6.0 RCP8.5 csiro-mk3–6-0.3 RCP2.6 RCP4.5 RCP6.0 RCP8.5 csiro-mk3–6-0.4 RCP2.6 RCP4.5 RCP6.0 RCP8.5 csiro-mk3–6-0.5 RCP2.6 RCP4.5 RCP6.0 RCP8.5 csiro-mk3–6-0.6 RCP2.6 RCP4.5 RCP6.0 RCP8.5 csiro-mk3–6-0.7 RCP2.6 RCP4.5 RCP6.0 RCP8.5 csiro-mk3–6-0.8 RCP2.6 RCP4.5 RCP6.0 RCP8.5 csiro-mk3–6-0.9 RCP2.6 RCP4.5 RCP6.0 RCP8.5 csiro-mk3–6-0.10 RCP2.6 RCP4.5 RCP6.0 RCP8.5 EC-EARTH consortium ec-earth.2 RCP4.5 ec-earth.6 RCP8.5 ec-earth.8 RCP2.6 RCP4.5 RCP8.5 ec-earth.12 RCP2.6 RCP4.5 RCP8.5 LASG, Institute of Atmospheric Physics, Chinese fgoals-g2.1 RCP2.6 Academy of Sciences; and CESS, Tsinghua University RCP4.5 RCP8.5 fgoals-s2.2 RCP4.5 RCP8.5 fgoals-s2.3 RCP8.5 The First Institute of Oceanography, SOA, China ﬁo-esm.1 RCP2.6 RCP4.5 RCP6.0 RCP8.5 ﬁo-esm.2 RCP2.6 RCP4.5 RCP6.0 RCP8.5

Table 1. (continued) Institution Model RCP Scenario(s) fio-esm.3 RCP2.6 RCP4.5 RCP6.0 RCP8.5 Geophysical Fluid Dynamics Laboratory (NOAA) gfdl-cm3.1 RCP2.6 RCP4.5 RCP6.0 RCP8.5 gfdl-esm2g.1 RCP2.6 RCP4.5 RCP6.0 RCP8.5 gfdl-esm2m.1 RCP2.6 RCP4.5 RCP6.0 RCP8.5 NASA Goddard Institute for Space Studies giss-e2-h-cc.1 RCP4.5 giss-e2-r.1 RCP2.6 RCP4.5 giss-e2-r.1 RCP2.6 RCP4.5 RCP6.0 RCP8.5 giss-e2-r.2 RCP4.5 giss-e2-r.3 RCP4.5 giss-e2-r.4 RCP4.5 giss-e2-r.5 RCP4.5 National Institute of Meteorological Research/Korea hadgem2-ao.1 RCP2.6 Meteorological Administration RCP4.5 RCP6.0 RCP8.5 Met Office Hadley Centre hadgem2-cc.1 RCP4.5 RCP8.5 Met Office Hadley Centre; Instituto hadgem2-es.1 RCP2.6 Nacional de Pesquisas Espaciais RCP4.5 RCP6.0 RCP8.5 hadgem2-es.2 RCP2.6 RCP4.5 RCP6.0 RCP8.5 hadgem2-es.3 RCP2.6 RCP4.5 RCP6.0 RCP8.5 hadgem2-es.4 RCP2.6 RCP4.5 RCP6.0 RCP8.5 Institute for Numerical Mathematics inm-cm4.1 RCP4.5 RCP8.5 Institut Pierre-Simon Laplace ipsl-cm5a-lr1 RCP2.6 RCP4.5 RCP6.0 RCP8.5 ipsl-cm5a-lr2 RCP2.6 RCP4.5 RCP8.5 ipsl-cm5a-lr3 RCP2.6 RCP4.5 RCP8.5 ipsl-cm5a-lr4 RCP6.0 RCP8.5 ipsl-cm5a-mr1 RCP2.6 RCP4.5 RCP6.0 RCP8.5 ipsl-cm5b-lr1 RCP4.5 RCP8.5

Table 1. (continued) Institution Model RCP Scenario(s) Japan Agency for Marine-Earth Science and miroc-esm.1 RCP2.6 Technology, Atmosphere and Ocean Research RCP4.5 Institute (The University of Tokyo), and National RCP6.0 Institute for Environmental Studies RCP8.5 miroc-esm-chem.1 RCP2.6 RCP4.5 RCP6.0 RCP8.5 Atmosphere and Ocean Research Institute miroc-5.1 RCP2.6 (The University of Tokyo), National Institute for RCP4.5 Environmental Studies, and Japan Agency for RCP6.0 Marine-Earth Science and Technology RCP8.5 Max Planck Institute for Meteorology mpi-esm-lr.1 RCP2.6 RCP4.5 RCP8.5 mpi-esm-lr.2 RCP2.6 RCP4.5 RCP8.5 mpi-esm-lr.3 RCP2.6 RCP4.5 RCP8.5 mpi-esm-mr.1 RCP2.6 RCP4.5 RCP8.5 Meteorological Research Institute mri-cgcm3.1 RCP2.6 RCP4.5 RCP8.5 Norwegian Climate Centre noresm1-m.1 RCP2.6 RCP4.5 RCP6.0 RCP8.5 noresm1-me.1 RCP2.6 RCP4.5 RCP6.0 RCP8.5

aThe ensemble was based on the Coupled Model Intercomparison Project Phase 5 (CMIP5) and includes four different representative concentration pathways (RCPs) that simulate increases in global mean radiant forcing between 2.6 and 8 W m22.

temperature-dependent allochthonous contributions and the temperature-independent autochthonous production.

2.5. Conceptual Model of Recruitment Limitation A conceptual model is used to consider the effects of the changing climate on recruitment of C. diabolis. We assume the springtime period conducive to recruitment is limited by the availability of food in the early spring and by a threshold peak water temperature reached in the late spring [Hausner et al., 2013], and we

Table 2. Projected Temperature Offsets (C) Used in the Hybrid Delta Climate Projections Mid-Century (2040–2049) Projections Long-Term (2090–2099) Projections

Month Tenth Percentile Median Nineteenth Percentile Tenth Percentile Median Nineteenth Percentile Jan 1.40 1.56 1.93 2.37 2.84 4.05 Feb 1.40 1.49 1.61 2.21 2.82 3.40 Mar 1.34 1.42 1.39 2.13 2.66 3.21 Apr 1.15 1.50 1.68 2.06 2.86 3.76 May 1.60 1.76 1.90 2.38 3.06 4.28 Jun 1.67 1.89 2.10 2.40 3.31 4.72 Jul 1.67 2.06 2.38 2.01 3.42 5.30 Aug 1.88 2.18 2.52 2.29 3.67 5.44 Sep 2.00 2.09 2.62 2.65 3.55 6.12 Oct 1.87 1.89 2.35 2.58 3.76 5.66 Nov 1.50 1.68 1.82 2.19 3.08 4.74 Dec 1.39 1.54 1.61 2.34 2.84 3.80

use projections of both food supply and water temperatures to estimate the duration of this period. Histori- cal population indices show the greatest gains between May and July. Given the size of the pupfish observed and the growth rates of C. diabolis [James, 1969], these increases reflect hatches that occur in March and April. We therefore assume that the recruitment window begins when the combined rate of food input and production (the sum of allochthonous and autochthonous contributions) reaches 156 kJ mo21 (the rate on 1 March, based on Wilson and Blinn’s [2007] data). Because the tolerance of Cyprinodon spp. for high temperatures is lowest during the egg stage and greatest in young fish [Shrode, 1975], we focus the recruitment window on the temperatures at which eggs have greater hatching success. Fish adapted to a constant temperature exhibit much lower ranges of thermal tolerance than fish subjected to fluctuating temperatures, and warm-adapted fishes show even lower ranges of tolerance than cold-adapted fishes [Otto and Gerking, 1973]. We therefore assume that the recruitment window ends when the mean water column temperature exceeds 33.5C, the constant temperature of the deep pool of Devils Hole. Neither the food threshold nor the temperature threshold acts as a direct physiological constraint on the C. diabolis population. Instead, they provide reference values that can be used to consider the timing of seasonal events in the ecosystem. In the same way that the population surveys offer a relative abundance that can be examined over time rather than a direct count of individuals, the timing of the food and temperature threshold allows us to consider the duration of the recruitment period across a range of climate scenarios. Using simultaneous projections of food and water temperature, we estimate the duration of the springtime recruitment window for each of the eight simulated scenarios (1980–1990, 2000–2010, three climate projections covering 2040–2049, and three climate projections for 2090–2099).

3. Results 3.1. Climate Change in the Mojave Desert Temperatures in the Mojave Desert have increased over the past 50 years. Spatially distributed PRISM records (PRISM Climate Group, Oregon State University, http:/prism.oregonstate.edu, created 4 February 2004) show mean annual temperature increasing approximately 0.02Cyr21 from 1960 to 2012 (n 5 53, R2 5 0.57, p < 1025); 30 year monthly temperature normals show an increase in annual average temperature of 0.48C between the 1971–2000 and 1981–2010 records. This trend is consistent throughout the year, with 10 of 12 months (all but February and December) showing increased maximum temperature and all 12 months showing increased minimum temperature. Local temperature observations from Amargosa Farms (approximately 20 km north of Devils Hole) also show a positive trend in average temperature 11 of 12 months (all but February) between 1966 and 2008, as well as a still greater statistically signiﬁcant 0.035Cyr21 increase (n 5 25, R2 5 0.51, p < 0.01) in annual average temperature (25 of 43 years had a complete annual temperature record) over the same time period (NOAA Cooperative Observer station 210650, accessed 15 October 2012).

3.2. Energy Fluxes and Water Temperatures in Devils Hole Changing climate influences the heat fluxes on the shallow shelf in different ways (Figure 2). Energy entering the water column on the shallow shelf is insensitive to air temperature—absorption of shortwave radiation is controlled by the elevation and angle of the sun, while the advective heat flux (exchange between the shelf and the constant-temperature reservoir below it) is controlled by the relative temperatures of the water column and groundwater reservoir below the shelf. Heat fluxes leaving the water column through the surface exhibit a much greater response to changes in the ambient air temperature; latent heat flux, sensible heat flux, and net radiative heat fluxes all depend in part on the air temperature above the water column. Averaged over the entire year, the magnitude of the surface heat flux is reduced by approximately 8Wm22 for every 1C increase in ambient air temperature. The net surface energy flux is the primary cool- ing mechanism for the shallow shelf, and the reduced magnitude leads to warmer water on the shelf. Dur- ing the warmest parts of the day, warmer water on the shelf suppresses exchange with the groundwater reservoir below, further increasing the mean temperature of the water column. Daily peak water temperatures are more sensitive to meteorological conditions than the daily means. When water column temperatures are near daily maxima, the water column is warmer than the groundwater and exchange with the reservoir below results in heat leaving the water column. Because this exchange is

a) Heat flows entering the water column buoyancy-driven, however, 1500 Advection warmer water on the shelf Shortwave Radiation 1000 reduces the advective ﬂux and minimizesthiscoolingeffect 500 during the hottest times of the day (Figure 3). Figure 3a dem- Heat Flow (W) 0 onstrates the enhanced circula- −500 12:00 18:00 00:00 06:00 tion during early morning from Time of Day the deeper waters, which is b) Heat flows leaving the water column 0 damped later in the day as the water column warms at the sur- −100 Latent Heat Net IR Radiation face (Figure 3b). At these times, −200 Sensible Heat the net surface ﬂux is also sup- −300 pressed by the elevated air

Heat Flow (W) −400 temperature. While the water temperatures are somewhat −500 12:00 18:00 00:00 06:00 less predictable during the Time of Day mornings and evenings, the c) Simulated Water Column Temperatures 33.6 daily peak temperatures clearly Present T + 3 °C 33.5 T + 1 °C T + 4 °C respond to the ambient air tem- T + 2 °C T + 5 °C perature (Figure 2c). 33.4

33.3 3.3. Conceptual Model of the Changing Ecosystem Temperature (°C) 33.2

33.1 Temperature simulations of his- 12:00 18:00 00:00 06:00 torical and present conditions Time of Day (Figure 4) show that the 1C Figure 2. Simulated energy flows and water temperatures in Devils Hole over a 24 h period local change in annual mean air based on average meteorological conditions observed during April 2010. (a) Energy enter- temperature over the past 30 ing the water column. (b) Energy leaving the water column. (c) Simulated temperatures based on April 2010 (blue line) and with assumed air temperature increases as indicated in years has driven a change of up the legend. In Figures 2a and 2b, positive heat fluxes indicate energy entering the water col- to 0.1C in peak water tempera- umn and negative values indicate energy leaving the water column. tures. In the long term, a projected increase of 3.2Cin annual air temperature causes an increase of up to 0.4C from historical peak daily water temperature. As the period of suitable water temperatures shifts to earlier in the year, the seasonal food web becomes more important. Simulations of daily peak temperature under the four different climate scenarios show an increase in peak temperatures from late spring to early autumn (Figure 5). Under current conditions, the period of optimal recruitment temperatures ends 6 days earlier than it did during the historical period with a stable population, and by the middle of the 21st century that period is projected to end almost 3 weeks earlier than it has historically. While the period of suitable water temperatures is ending earlier, allochthonous nutrients are also becom- ing available earlier in the spring. Food web projections (Figure 6) show that more food is expected to be available earlier in the spring, allowing recruitment to begin earlier in the year. However, the projected food supply does not increase fast enough to keep pace with the earlier crossing of the temperature threshold that defines the end of the recruitment period. While the mid-century projections show threshold temperatures being reached 3 weeks earlier than in the historical period, sufficient food becomes available just 1 week earlier. The differential responses of the physical habitat and the food web further limit the already- short period during which conditions in the ecosystem are thought to be most conducive to springtime recruitment. The durations of the optimal recruitment period under historical, present, median midrange climate, and median long-range climate scenarios are 74, 68, 63, and 65 days, respectively. Uncertainty in the climate projections can affect both the beginning and ending of the optimal recruitment window (Table 3). In the 2040–2049 projections, the annual optimum begins on 23 February, regardless of the climate scenario selected. Climate uncertainty plays a greater role in the temperature threshold, resulting in an uncertainty of 62 days for this threshold. In the 2040–2049 simulations, the period of primary recruitment

a) 0900 h lasts between 61 and 66 34.0 0.05 33.8 days, with a duration of 0.10 0.15 33.6 63 days under median 0.20 33.4 projections. As climate

Depth (m) 0.25 33.2 0.30 projections are made 33.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 over a longer time scale, Easting (m) b) 1430 h the uncertainty increases. 34.0 0.05 In the 2090–2099 simula- 33.8 0.10 tions, the annual opti- 0.15 33.6 mum begins between 17 0.20 33.4

Depth (m) 0.25 33.2 and 21 February and 0.30 33.0 ends between 19 and 26 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 Easting (m) April; the duration of the annual optimum for the Figure 3. Simulated circulation on the shallow shelf during (a) morning low temperature and (b) 2090–2099 simulations afternoon peak temperatures. Water can ﬂow between the shallow shelf and the reservoir below through a vent (easting 1.94–2.14 m); the remainder of the shelf (easting 0–1.94 m, indicated by the ranges from 57 to 68 heavy black line) is bedrock. Note at 0900 h the plume of warm water entering the system through days, with a duration of the vent and the high vertical velocities along the east side of the domain, and the absence of that 65 days under the ﬂow at 1430 h. median projection.

4. Discussion The shortening period of conditions suitable to recruitment offers a mechanism by which the changing climate may negatively affect the annual recruitment of C. diabolis, leading to a population decline. The change in timing is consistent a) 1980−1990 with observations—previous 34.5 studies of C. diabolis noted ) 34 later peaks in larval popula- 33.5 tions than more recent work. Temp. (°C 33 James [1969] reports that more J F M A M J J A S O N D Month newly hatched ﬁsh were pres- b) 2000−2010 ent March–June than during 34.5

) other times of the year, with a 34 peak spawning on 26 April. 33.5 Despite small sample sizes, Temp. (°C 33 Minckley and Deacon [1973] J F M A M J J A S O N D Month observed similar results, with c) 2040−2049 peaks in both mean egg diam- 34.5

) eter and maturity index (100* 34 ovary weight divided by ﬁsh 33.5

Temp. (°C weight) in May; both metrics 33 J F M A M J J A S O N D were lowest in fall and winter. Month Between December 1975 and d) 2090−2099 34.5 August 1976, Deacon et al. ) 34 [1995] observed 31 larvae on the shallow shelf, with all but 33.5

Temp. (°C seven in April (13 larvae) and 33 J F M A M J J A S O N D May (11 larvae) 1976. In more Month recent work, more larvae have

Figure 4. Simulations of peak daily water temperature on the shallow shelf for (a) historical been observed earlier in the conditions (1980–1989), (b) present conditions (2000–2009), (c) midterm projections (2040– year—in 1996 and 1997, larvae 2049), and (d) long-term projections (2090–2099). (c, d) For the projected temperatures, the observations peaked in early shaded area indicates the range of water temperatures simulated based on the 10th and 90th percentile temperature projections, while the solid lines indicate the simulations based April rather than the expected on the median projected temperature. peaks in May [Gustafson and

34.5 1980 − 1990 Deacon, 1997], and 2004 larvae col- 2000 − 2010 2040 − 2049 lections peaked in March and April, 2090 − 2099 Recruitment temperature with a sharp decline between 9 April and 1 May 2004 [Lyons, 2005]. 34.0 The uncertainties inherent in climate projections increase with the temporal range of those projec-

33.5 tions, and the hybrid delta approach accounts for that uncer- Peak Daily Water Temperature (°C) tainty. For all of the mid-century simulations, food availability is pro- 33.0 J F M A M J J A S O N D jected to begin the same day, and Month of Year the temperature threshold is pro-

Figure 5. Daily peak water column temperatures under historical (1980–1990), pres- jected between 25 and 29 April ent (2000–2010), median midrange (2040–2049), and median long-range (2090– resulting in an uncertainty of 4 2099) climate scenarios (note that the month labels correspond to the middle of days in the duration of the optimal each month). The shaded area labeled ‘‘recruitment temperature’’ is the water column temperature at which the conceptual model assumes the majority of successful recruitment window. As the tem- recruitment occurs, and the points at which the water column temperature traces poral range of the projections leave this area (indicated by the red markers) represent the end dates of the recruitment window. increases, the uncertainties in both the projected food threshold (19 February 62days)andthepro- jected temperature threshold (between 19 and 26 April) increase. The increased uncertainty of the long-range climate projections results in a more uncertain window of recruitment, but even the most conservative estimate for 2090–2099 (68 days) is shorter than it was during the historically stable period. The model simpliﬁes a complex ecosystem, neglecting both competing hypotheses to explain the population decline (previously mentioned) and a number of factors relevant to the inherent assumptions of the model itself. These assumptions, however, are generally conservative. Plentiful DO has been shown to mitigate the effects of elevated temperatures, accelerating embryo development in Cyprinodon macularius [Kinne and Kinne, 1962] such that early hatching larvae are more developed in highly oxygenated waters than in hypoxic conditions. Oxygen dynamics in Devils Hole are very different than in typical limnological systems—the inﬂuent groundwater has a relatively constant DO of 2.5–3.0 mg L21, and the low surface area to volume ratio limits oxygen- ation through surface agitation. 1000 1980 − 1990 Instead, the primary source of DO is 900 2000 − 2010 2040 − 2049 photosynthesis, and DO on the shal- 2090 − 2099 800 Sufficient Food low shelf exhibits both diel and sea- 700 sonal cycles in proportion to the 600 rate of primary productivity [Baugh

500 and Deacon, 1983b; Deacon et al., 1995]. Because the primary produc- 400 tivity that generates DO is limited Monthly Food Supply (kJ) 300 by solar exposure, the seasonal DO 200 cycle will remain stationary while 100 springtime water temperatures 0 increase. The combination of ele- J F M A M J J A S O N D Month of Year vated springtime water temperatures and unchanging seasonal DO Figure 6. Rate of total food availability on the shallow shelf under historical (1980– 1990), present (2000–2010), median midrange (2040–2049), and median long-range variations may further restrict the (2090–2099) climate scenarios (month labels again correspond to the middle of each primary period of successful recruit- month). The shaded area in this plot indicates the rate of food availability assumed to ment. Although higher water tem- be necessary to sustain recruitment, and the points at which the food availability curves enter this area (indicated by the red markers) represent the beginning dates of peratures can be mitigated by the recruitment window. elevated DO, the shift to earlier

threshold temperatures reduces Table 3. Dates on Which Thresholds Are Exceeded and Periods of Optimal Recruitment Conditions Under a Range of Climate Scenarios the likelihood that this mitiga- Date of Food Date of Temperature Duration of tion will occur. As threshold Scenario Threshold Threshold Window (days) water temperatures occur earlier Historical (1980–1990) 1 Mar 14 May 74 in the year, the lower available Present (2000–2010) 27 Feb 6 May 68 DO will be less effective at miti- Midterm (2040–2049), 23 Feb 29 Apr 65 gating the effects of higher 10th percentile Midterm (2040–2049), median 23 Feb 27 Apr 63 water temperatures. Midterm (2040–2049), 23 Feb 25 Apr 61 90th percentile Further, in the early springtime, Long term (2090–2099), 21 Feb 26 Apr 68 increased air temperatures may th 10 percentile not necessarily result in an Long term (2090–2099), median 19 Feb 25 Apr 65 Long term (2090–2099), 17 Feb 19 Apr 57 increase in the allochthonous 90th percentile nutrition available to C. diabolis. Seasonal convection in the deep pool of Devils Hole is driven by cooler water flowing from the shallow shelf and displacing warmer water in the aquifer below it [Hausner et al., 2012]. Even as allochthonous contributions from the atmosphere increase, this convection may remove some of that additional material to the deep pool where it would be unavailable to newly hatched larvae. Furthermore, the strong correlation between current allochthonous inputs to Devils Hole and temperature does not necessarily indicate a causal relationship. Springtime allochthonous inputs comprise primarily insect carcasses and plant material [Wilson and Blinn, 2007]. Insect activity may respond to temperature, but may also be controlled by photoperiod, which does not change with climate [Tauber and Tauber, 1981]; vegetation growth is influenced by both temperature and water availability, and seasonal changes in precipitation are therefore likely to affect allochthonous nutrition. Springtime precipitation in the Mojave Desert is expected to decline during this century [Karl et al., 2009], and such a decline might decrease rather than increase the amount of allochthonous material entering the ecosystem in the spring. Finally, the food supply may not be driving the onset of recruitment at all—while C. diabolis engage in spawning activity year-round, their activity increases in the spring [Riggs and Deacon, 2004], and may be linked to external cues (e.g., photoperiod) rather than the availability of food. The conceptual model pre- sented here is conservative, and may in fact overestimate the positive shift in the beginning of the period of recruitment. The conceptual model also neglects the time scales on which the ecosystem responds to climate—the model considers seasonal temperature variations, but water temperatures exert reproductive stresses on a daily basis. Exposure to 39C water for as little as 3 h results in complete mortality in C. n. nevadensis eggs [Shrode, 1975], and embryos exposed to 36C were often underdeveloped and unable to swim upon hatching [Shrode, 1975] and therefore unlikely to survive to adulthood. C. n. nevadensis eggs incubated at a constant 32C successfully hatch approximately 4 days after fertilization [Shrode, 1975]. If embryos in Devils Hole are exposed to 4 days of temperature variability before hatching and the daily peaks in mid-April last for approximately 150 min, one hot April day has the potential to affect the eggs fertilized over the 3 days preceding it as well as those fertilized that day. The practical effect of this sensitivity is that the conceptual model of recruitment limitation is again conservative and that the period of suitable temperatures for successful egg hatching likely ends earlier than indicated. In 2006, adult Devils Hole pupfish were observed showing signs of emaciation, and in December 2006 the managing agencies began a supplemental feeding program for C. diabolis. More recently, vertical habitat was added to the shallow shelf in 2013 to provide some shaded shelter from high temperatures and additional habitat for early-life-stage fish. Both of these actions have the potential to mitigate the impacts of climate on the ecosystem. Although active management adds uncertainty to the future projections of the recruitment period, the present and historical scenarios remain unchanged, and the conceptual model of recruitment limitation offers a conservative baseline for habitat comparisons over time. Further work in Devils Hole is necessary to establish whether a causal link exists between climate and the C. diabolis population, as well as to consider alternative hypotheses that may have contributed to the population decline, improving the ability of the managing agencies to conserve the remaining population of Devils Hole pupfish.

5. Conclusions Cyprinodon diabolis, which was exposed to temperatures near its thermal maximum even before recent climate change, is now subject to increasing water temperatures. In this work we have demonstrated a mechanism by which climate can negatively affect the viability of the C. diabolis population. Although we cannot show deﬁnitively that climate change is responsible for the post-1995 population decline, the changing climate of the Mojave is driving conditions that will likely reduce the success of annual recruitment of young C. diabolis in the future. Endemic and physically isolated species like C. diabolis that have evolved in specialized habitats are far more vulnerable due to global climate change than are species with the ability to shift to more favorable climatic conditions. The sensitivity of Devils Hole to climate change means that the ecosystem can serve as a bellwether, providing an early indicator of ecological effects of climate change. The ecosystem’s fast response to climate changes, however, also affords the opportunity to quickly examine the effects of climate change on a desert aquatic ecosystem, and the use of CFD modeling in conjunction with ecological analysis offers one approach to examining the complex interactions between climate changes and ecology.

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