Development of a risk model to determine the expansion and potential environmental impacts of Asian clams in Lake Tahoe. Dr. Marion E. Wittmann1*, Dr. Sudeep Chandra2, Dr. John E. Reuter1, Dr. S. Geoffrey Schladow1, & Marianne Denton2** With text contributed from papers that have been submitted for publication with the following scientists Annie Caires2, Mario Acost3, Dr. Francisco Rueda3, Dr. Andrea Hoyer3, Jon Gardner1, and Dr. Jeffrey Baguely2 A final report submitted to the US Forest Service and the Nevada Division of State Lands June 2013 Please cite this report as follows: ME Wittmann, S Chandra, JE Reuter, SG Schladow, M Denton, A Caires, M Acosta, F Rueda, A Hoyer, J Gardner, and J Baguely. 2013. Development of a risk model to determine the expansion and potential environmental impacts of Asian clams in Lake Tahoe. 1
Tahoe Environmental Research Center, University of California, Davis 2
Aquatic Ecosystem Analysis Laboratory, University of Nevada, Reno 3
Instituto del Agua, Universidad de Granada, Spain *
Present address: University of Notre Dame **
Present address: Nevada Division of Environmental Protection 1
Table of Contents General Background………………………………………………………………………………………………………………..3 Key questions addressed in this study……………………………………………………………………………………..6 What are the environmental parameters that contribute to the establishment and expansion of existing clam beds?.................................................................................................7 What is the life history and growth of clams in oligotrophic Lake Tahoe?..................................29 What is the role of lake currents in the transport of larval/juvenile stages to noninfested locations?.....................................................................................................................................46 What are the potential impacts to the lake’s algal community?.................................................71 Acknowledgements…………………………………………………………………………………………………………………79 References………………………………………………………………………………………………………………………………80 2
BACKGROUND The invasive freshwater bivalve Corbicula fluminea (Asian clam) is currently established in Lake Tahoe. Since its initial introduction in Washington State along the Columbia River in the late 1930s, its spread in the United States has been both rapid and extensive – it now is found in water bodies in 38 states. Because of its economic and ecologic effects it is considered to be the most important aquatic non-­‐indigenous aquatic animal in North America (McMahon 1999). Surveys of the animals living in the lake sediments by the California Department of Fish and Wildlife (CDFW formerly named the California Department of Fish and Game) and the Nevada Department of Wildlife (NDOW) in the 1960’s did not find Asian clam in Lake Tahoe. Asian clam were first detected in the Tahoe region in 1945 in the Sacramento-­‐San Joaquin drainages and by 1981 the Pyramid Lake Paiute Tribe noted its presence on the Lower Truckee River. Surveys by co-­‐PI Chandra in 2005-­‐2006 showed that clam densities were variable (4 to -­‐2
212 clams m ) in the Lower Truckee River (Clark, Wadsworth, Nixon) and its tributaries (North Truckee Drain and Steam Boat Creek). Asian clam was first observed at Lake Tahoe in very low numbers in 2002 (Hackley et al. 2008). In 2003, Dave Herbst (UC Santa Barbara) positively identified Asian clams collected from Nevada Beach at Lake Tahoe. Shells had been collected by a citizen and sent to Herbst via the League to Save Lake Tahoe with a report of this sighting was first published in the League’s summer 2003 Newsletter (Figure 1). By 2008, clam population had apparently increased to a number that were detectably by the general public in the Southeastern part of the lake, with white colored, shells of dead organisms laying on the sediment. The current known distribution (area ~1 million m2, 250 acres) is patchy along the southeast shore from Zephyr Cove (NV) to El Dorado Beach (CA) is changing due to its rapid growth rate and ability to colonize in the abundant sandy bottom. Asian clam has both pelagic and benthic life stages, enabling it to possibly spread long distances by boats and lake currents and locally by diffusive growth. Management concerns for Lake Tahoe and surrounding water bodies are focused on the effective in-­‐lake control of Asian clam as well as the prevention of future quagga stages, enabling it to possibly spread lake-­‐wide by boats and lake currents and locally by diffusive growth. Management concerns for Lake Tahoe and surrounding water bodies are focused on the effective in-­‐lake control of Asian clam as well as the prevention of future quagga and zebra mussel introduction and establishment. The information in this current report is intended to advance the scientific literature in invasion biology and to assist managers in developing a management plan for Asian clam in Lake Tahoe. Asian clams have high dissolved oxygen requirements (anoxia intolerant), can tolerate salinities of up to 13 ppt, and temperatures between 2 and 30°C. While there has been no definitive study on the influence of calcium concentrations on Asian clam, it appears that at concentrations of 6 mg L-­‐1 and a pH > 6.5 populations has enabled viable populations of Asian clams on the Roanoke River in North Carolina (Cooper 2007). Dissolved calcium levels in Lake Tahoe are consistently in the range of 8-­‐10 mg L-­‐1 with elevated concentrations within clam beds. Spawning can occur year round but water temperature must be above 16°C for larval release; a single clam can release an average of 400 juveniles per day, with reproductive rates highest in the fall (Aguirre and Poss 1999). 3
In 2008, researchers and managers in the Tahoe basin became increasingly concerned at Figure 1. League to Save Lake Tahoe newsletter (Spring 2003) identifying clams as Corbicula fluminea. the apparent expansion of clam beds in the southeastern part of Lake Tahoe and the possible link to eutrophic conditions developing resulting in a large scale visible algal bloom (Wittmann et al. 2008). Concerned about the impact of these clams on the lake’s ecology, researchers responded immediately to requests from agency staff and local citizens by conducting a rapid, preliminary assessment of the infested area in Lake Tahoe. Preliminary data collected by the Universities in late summer 2008, suggests that clams are found buried in mostly sandy sediments with 70 to over 3000 individuals m-­‐2 in the top 10-­‐15 cm of substrate. Current densities have been observed at > 8000 individuals m-­‐2. Clams were found in the surficial sediments as deep as 70 m at Nevada Beach, however, with highest densities observed between 3 and 10 m. Asian clams have not been found at depths greater than 39 meters in any transects taken to date, although they have been found at >40 m depth in a variety of sediment types (silt, mud, gravel) in other systems such as Lake Mead (Chandra and Wittmann, unpublished data). Laboratory experiments conducted in September 2008, showed that Asian clams collected from Lake Tahoe excrete nitrogen and phosphorus at levels, several orders of magnitude greater than ambient lake levels (Figure 2). Ammonium concentrations were approximately 4 µg/L higher in clam beds than in sediments without clams. Similarly, SRP concentrations were approximately 1.5 µg/L higher in clam beds. The excreted nutrients resulted in a threefold increase of phytoplankton algae in bioassay experiments. While Asian clams are capable of aggressive filter feeding of pelagic algae as well as substrate feeding within sediments, it is unknown at what rate and which mode of feeding Asian clam prefer in Lake Tahoe. This feeding strategy should influence both water quality and sediment condition. Using the results of the excretion experiments, a clam population of approximately 900 individuals m-­‐
2
would be required to support the nuisance levels of the filamentous green metaphyton, Zygnema, that were found associated with the existing clam beds, The fact that the in situ 4
populations were in this range provides further weight of evidence that the Asian clam can have an effect on bottom algae growth in Lake Tahoe. The ability of invasive bivalves to influence light penetration, deliver nutrients to the lake benthos, and influence the growth of metaphyton and other benthic plants has been previously reported for other ecosystems (e.g. Lowe and Pillsbury 1995; Bosch et al. 2001). (µg/L)
15
10
5
0
No Clams
Clams
Ambient lake level
No Clams
Clams
Ambient lake
level
6
(µg/L)
4
2
0
Figure 2. Surficial sediment pore water concentrations in concentrated clam beds from Marla Bay in 2008, areas adjacent to beds with no clams, and lake water collected above beds. a) ammonium concentrations (ug L-­‐1) and b) soluble reactive phosphorus concentrations (ug L-­‐1). By altering water column and substrate characteristics, as well as by adding substrate on which to colonize, Asian clam may facilitate the invasion of other invasive bivalve species such as dreissenid mussels, quagga and zebra, invaders found within the region at Lake Mead, the lower Colorado River, and Central California. September 2008 surveys suggest live Asian clam beds have elevated concentrations of sediment porewater nitrogen and phosphorus compared to sediments where clams are absent. Additionally, a sediment porewater calcium sample taken from within a pile of dead clam shells show dissolved calcium level of 26 ppm -­‐ twice as high compared to areas without shells. In addition to showing signs of local environmental impacts, 5
Asian clam are also actively reproducing in Lake Tahoe, with a large abundance of juveniles and generational cohorts found in summer surveys. In other systems, Asian clam juveniles (<14mm) are capable of long distance dispersal via transport in the water column. A one-­‐time deployment of surface current trackers in August showed that currents circulate through Marla Bay and head to other portions of the lake within 24 hour periods. Predicting future spread of Asian clam will rely on an understanding of nearshore and lake-­‐wide surface current transport. KEY QUESTIONS ADDRESSED IN THIS STUDY The findings collected to date highlight the need for a risk assessment model that predicts future spread of Asian clams. We have worked with the Lake Tahoe Aquatic Invasive Species Coordination Committee, Asian Clam Working Group, and other agency representatives to develop pilot projects to test various control methods (bottom barriers, suction removal) on Asian clams (e.g. Wittmann et al. 2010, 2011, 2012a,b). In this report we focus on understanding key questions needed for a risk assessment of Asian clam distribution at the Lake Tahoe. Specific questions that will be addressed include: A. What are the environmental parameters that contribute to the establishment and expansion of existing clam beds? B. What is the life history and growth of clams in oligotrophic Lake Tahoe? C. What is the role of lake currents in the transport of larval/juvenile stages to noninfested locations? D. What are the potential impacts to the lake’s algal community? E. What are the environmental factors that contribute to the establishment and expansion of clam beds? The answers to these and other questions based on numerous research studies since 2008 have been instrumental in allowing resource managers and scientists to work together in developing an Asian clam control program. 6
A. What are the environmental parameters that contribute to the establishment and expansion of existing clam beds? Background of initial clam observations in Lake Tahoe, reason for concern The first observations of Asian clam in Lake Tahoe occurred in southeastern regions of Lake Tahoe in 2002 with relatively low population density estimates (2 to 20 clams m-­‐2; Chandra unpublished data). In spring 2008 University of California, Davis researchers Scott Hackley and Brant Allen discovered extensive and often dense beds of Asian clam in nearshore areas from Zephyr Cove to El Dorado beach. Depth surveys conducted in summer 2008 by the UC Davis – Tahoe Environmental Research Center (TERC) and University of Nevada, Reno (UNR) science team showed that Asian clam were well established and expanding their range compared to the initial 2002 discoveries. TERC and UNR researchers also observed that Asian clam distribution in -­‐2
Tahoe was spatially patchy in space, with areas of very high clam density (>2000 individuals m ) -­‐2
located next to areas with no or low densities (<100 individuals m ). This patchiness is likely a result of differences in bottom substrate type, food availability, light penetration, temperature and other environmental variables (McMahon and Bogan 2001). The objective of this section is to understand the survival strategies of Asian clam in Lake Tahoe through the development of a relationship between measured Asian clam populations and the environmental variables associated with them. Methods We have carried out this work by collecting quantitative field measurements via benthic surveys of Asian clam populations in multiple locations (Marla Bay, Zephyr Cove, Nevada Beach, Lakeside and other south shore sites) and have quantified habitat characteristics including sediments, food availability, temperature, ultra violet light and photosynthetically active radiation, nutrients (nitrogen, phosphorus, carbon). To determine the key explanatory factors in Asian clam distribution in Lake Tahoe, we analyze the relationship between Asian clam population and habitat variables using generalized linear models and select optimal models using robust information criterion. The following section details the methodologies and a brief rationale for collections employed in this part of the study. Benthic surveys and distribution in space on surficial sediment across the lake at established locations and within the sediment column To characterize and track dynamics of population distribution and density of Asian clams, an extensive and repeated sediment grab sampling survey was employed from 2008 through 2009. A total of 194 sediment grabs were collected in October 2008, May 2009 and October 2009 from 0-­‐40 or 0-­‐80 m depths at 10 m intervals at Marla Bay, Zephyr Cove and Nevada Beach. These locations were chosen because they were the original sites of Asian clam observation in spring 2008 (Hackley et al. 2008) and because they represented a range of Asian clam population densities, sediment substrates and shorezone use types. 7
A petite Ponar grab sampler (Wildco, 2.4 L volume, 231 cm2) was used to collect the sediment grab samples. The Ponar was deployed from a small boat and once the sample was collected, it was placed in a 500-­‐µm mesh sieve bucket and sieved immediately to remove silt. Samples were then placed in a labeled Ziploc bag and stored on ice in a cooler until they were further processed at the lab. The sieve chamber was visually inspected after each sample was removed to ensure no invertebrates were left in bucket. If the Ponar misfired or only a half sample was collected, it was discarded and resampled. Samples were returned to the laboratory for processing and identification. In the laboratory sediment samples were elutriated to separate invertebrates from the sediment matrix. During the elutriation process, contents of one sample bag were emptied into a 20 L (5-­‐gallon) container. The bag was rinsed over the bucket to ensure no materials or invertebrates are lost. A solution of sugar, saturated to lake water, was then added to the sample until the bucket is approximately 1/8 full (Anderson 1959). The bucket was picked up and swirled numerous times to float invertebrates to the top of the solution. While the water was still in motion, it was poured into a 500-­‐µm sieve. This process was repeated 5 to 10 times. Invertebrates and sediment caught by the sieve were then transferred to a container for invertebrate removal from sediments. Each container was visually inspected for Asian clams. Native invertebrates were kept in separate vials. Each sample was inspected by at least three people to ensure the removal of all invertebrates from the sediment substrate. Sediments were then discarded and the processed samples were stored in a cool place until the invertebrates can be counted and identified utilizing Thorp and Covich (2001) and Merrit and Cummins (1996). Invertebrates were removed from the sediment samples the same day of collection to minimize fatality and/or damage to biota. Two measurements were taken for each clam using digital calipers. Measurement 1 is the longest distance from the umbo, or shell hinge, to the edge of the shell opening. Measurement 2 is the longest distance from the anterior to posterior edge (Figure 3). These measurements were carried out for both Asian clam and the native pea clam (Pisidium spp.). After the measurements have been recorded, the clams were re-­‐preserved with 70 % ethanol solution and stored for future analysis, if needed. 8
a.
b.
Figure 3: Measurements recorded for Asian clams during processing and data collection. Panel a represents Measurement 1 and Panel b represents Measurement 2. Asian clams are able to burrow within sediments. To measure the vertical distribution of Asian clam within the sediment column, cylindrical hand cores were collected from Marla Bay and Lakeside by pounding 18-­‐inch lengths of 2-­‐inch diameter PVC pipe into the sediment. Hand cores were immediately plugged with stoppers underwater to preserve the stratigraphy of the sample and placed in a -­‐80⁰C freezer the same day. After thoroughly frozen, the hand cores were cut into 2 cm sediment segments with a table mitre saw. All clams found at each 2 cm depth interval were counted and measured with digital calipers. Surficial sediment particle size distribution Surficial sediment samples (n = 3) were collected from five sites (three with known populations of Asian clam: Marla Bay, Lakeside, Nevada Beach, and two without known populations of Asian clam: Emerald Bay and Incline Village and analyzed for particle size distribution. At the time of this sampling, Asian clam populations in Emerald Bay were not yet discovered. Sediment material was stored refrigerated with lake water until the time of analysis. Sediment sieving protocol followed the techniques listed in Gordon et al. (1992). Organics were removed with hydrogen peroxide (Goudie 1981), fines dispersed with sodium carbonate and washed through a 0.0625 mm sieve and weighed. Sample remainders were dried over a 5-­‐day period in aluminum pans and dried aggregates were broken with rolling pin. A set of eleven sieves that follow the Wentworth particle size classification were used to sieve samples, decreasing in aperture size downward with a 1 Φ interval with a 4.5 Φ (0.0017 mm) as smallest size and -­‐4.7 Φ (64 mm) as largest Φ. The Krumbein phi Φ scale is a modification of the Wentworth scale is a logarithmic scale used to denote sieve identification and is computed by the equation: Φ = -­‐log2 D/D0 Equation 1 where D is the diameter of the particle, and D0 is a reference diameter, generally 1 mm. Approximately 200 grams of sample was weighed and placed on the largest sieve, shaken for 10-­‐15 minutes, and then transferred from each sieve to a weighing tray for measurement. 9
Sediment particle sizes are presented as proportion of sample retained in each sieve (±S.E.) and represented in the regression model as M0, or median particle size. Water column nutrient and surficial sediment pore water concentrations Water column nutrient samples were collected monthly from 0 – 80 m water depth from 2008 – 2009 as part of the UC Davis MLTP monitoring program. This sampling included the following water column analytes: (1) nitrate (NO2-­‐ + NO3-­‐), (2) hydrolysable + ortho, total phosphorus (THP) and (3) total phosphorus (TP), both expressed as µg/l as N and P, respectively. These samples were analyzed using modified methods for low nutrient waters. A one-­‐time, high resolution sampling from 0 – 5 m water depth in Marla Bay, including surficial (1-­‐3 cm) sediment porewaters was carried out in 2008 to determine the full profile of sediment porewater and water column concentrations of ammonium (NH4+), soluble reactive phosphorus (SRP) and dissolved phosphorus (DP, which includes inorganic and organic dissolved P). Water samples were collected in the field, returned to the laboratory and filtered through a Whatman GF/C filter (0.7 µm affective pore size) and immediately stored in the freezer until nutrient analysis was carried out. From the frozen sediment core, the porewater was extracted and filtered through a Whatman GF/C filter and immediately stored in the freezer until nutrient analyses were carried out. Macronutrients were measuring using standard methods (American Public Health Association 2006, total and dissolved phosphorus-­‐ USEPA method 365.3, soluble reactive phosphorus-­‐ SM4500-­‐PE, nitrate-­‐ USEPA 353.1, ammonia/ ammonium-­‐ USEPA 350.1). Water column fluorescence, chlorophyll a and phaeophyton concentrations Asian clams and other filter feeders depend on both pelagic and benthic forms of algae as a main source of food. Chlorophyll is commonly used as a surrogate for algal biomass in many studies of water quality and limnology (Horne and Goldman 1994) and chlorophyll a is the most abundant form of living algal chlorophyll. When algae die, chlorophyll a degrades into phaeophytin. Measuring both chlorophyll a and phaeophytin these molecules is essential to understanding the food available to organisms that filter feed since they are nondiscrimantory in their uptake of particles. Chlorophyll a and phaeophytin concentrations were measured monthly from 2008 – 2009 at 10 m intervals from 0 – 80 m water depths. For pelagic algal monitoring, water samples collected in the field are returned to the lab and filtered through a 0.7 µm Whatman GF/C filter and frozen until ready for analysis with the methanol extraction technique used by UC Davis – TERC and described in Winder and Reuter (2009). Chlorophyll fluorescence was measured with a Turner Designs 10AU fluorometer. Once this fluorescence value is obtained, the sample was acidified with 0.050 ml of 0.3N HCl and allowed to sit for one minute before the fluorescence after acidification is measured. The acidification procedure converts live chlorophyll to phaeophytin. Chlorophyll a and phaeophytin values were calculated using the following equations: Chloropyll a (µg L-­‐1)=(r/(r-­‐1)) x (Rb-­‐Ra) x Vex/Vfil 10
Equation 2 Phaeophytin (µg L-­‐1)=(r/(r-­‐1)) x (rRb-­‐Ra) x Vex/Vfil Equation 3 where Rb was the fluorescence before acidification minus the average filter blank fluorescence for range used, Ra was the fluorescence after acidification minus the average filter blank fluorescence for range, Vfil was the volume filtered (L), Vex was the volume used for extraction (L), r was the calibration factor determined from calibration with pure chlorophyll a. Photosynthetically active radiation, ultraviolet light, and dissolved organic carbon Underwater light climate is one of the most important factors controlling the growth of aquatic plants, including phytoplankton (free-­‐floating algae), periphyton (attached algae), and macrophytes (rooted vascular plants). Phytoplankton and other algal species comprise almost the entirety of Asian clam food supply and are a key determinant in growth and survival (McMahon and Bogan 2001). Photosynthetically active radiation (PAR) represents the spectral range of solar light between the wavelengths of 400 and 700 nm. Since it is this range of wavelengths that support photosynthesis, PAR is a biologically meaningful measurement commonly used to characterize the optical properties of lakes and oceans. The spectral region of PAR corresponds more or less with the range of light visible to the human eye. The amount of PAR that fully penetrates into the water column affects both the rate of photosynthesis (or primary productivity) and the location/vertical extent of algae/plants from the surface to the bottom. In fact, the penetration of light into water bodies provides structure to these aquatic ecosystems. The “rule of thumb” is that algae and aquatic plants are able to grow down to a depth where the light intensity is approximately one percent of that at the surface (also called the compensation depth). This upper sunlit zone is called the euphotic or photic depth and is the depth of the water column that is exposed to sufficient sunlight for net primary production to occur, i.e. photosynthesis exceeds respiration (Wetzel 2001). The extent of the euphotic depth is a measure of water clarity and is typically only a few meters in nutrient rich lakes, but may reach almost 200 meters in the clearest portions of the open ocean. The extent of the euphotic depth in Lake Tahoe is at about 60 m (Swift 2004, TERC 2008). Ultraviolet light radiation (UVR, 280-­‐400 nm) is mutagenic, carcinogenic, and can be lethal to primary consumers with exposure (Williamson et al. 2001). It can control the vertical migration of organisms (e.g. Daphnia are negatively phototactic to UVR wavelengths (Storz and Paul 1998), there by influencing their energetics and availability for food supply. Lake Tahoe’s high transparency results in deep water UVR penetration. Photosynthetically active radiation (PAR, 400-­‐700 nm) and Ultraviolet light radiation (UVR, 280-­‐400 nm) data were used as indices for underwater solar radiation and were measured at 10 nearshore sites in the summer of 2009 (Table 2) with a BIC profiling UVR-­‐PAR radiometer (BIC Logger; Biospherical Instruments, San Diego, California, USA). This instrument quantifies incident solar irradiance at three different UVR wavelengths (305, 320, and 380 nm) as well as visible wavelengths of PAR. Transparency data from BIC profiles were used to calculate diffuse attenuation coefficients (Kd) for each site and were combined with cumulative surface irradiance data measured with a Biospherical Instruments BICLogger, a multichannel, internally recording radiometer of a similar design and specifications to the BIC, to estimate 11
total exposure for the duration of the incubation experiment. The 320-­‐nm UVR exposure at 1 m depth for a given site was calculated for a standard surface exposure. The method used for measuring UVR as well as the resulting data used in this report come from (Tucker et al. 2010). PAR was also measured at 10 m depth intervals from 0 to 80 m water depths from 2008 – 2009 using a SBE 25 SEALOGGER CTD, called ‘Seabird’ instrument (Seabird SBE 25 Sealogger CTD), that stores PAR measurement electronically. The PAR sensor/cable is lowered from the research vessel and measurements are made throughout the entire year with approximately 25-­‐30 individual profiles taken each year at the Index Station and approximately 13-­‐14 individual measurements taken at the Mid-­‐lake Station. Dissolved organic carbon (DOC) samples were collected at 10 sites in the nearshore of Lake Tahoe during the summer of 2009 (for a list of site locations see Table 2). Water samples were collected in pre-­‐rinsed 1-­‐L polyethylene bottles from within the mixed layer. Water used in DOC analysis was filtered through pre-­‐ashed Whatman GF/F filters within 8 hours of sample collection using a glass filter support. The filtered sample was stored in the cold and dark in 40-­‐
mL glass bottles until analysis. The DOC samples were analyzed with a Shimadzu TOCVCPH analyzer (Shimadzu, Columbia, Maryland, USA) within one week post sampling (Liston et al. 2013). Influence of temperature on potential growth One important conjecture for Asian clam establishment in new environments is temperature limitation. This conclusion is drawn from observations that continued exposure to temperatures below 1–2°C correlated with mortality (Mattice and Dye 1976; Rodgers et al. 1977; Morgan et al. 2003; Werner and Rothhaupt 2008; Weitere et al. 2009), and that the occurrence of Asian clams in rivers such as the St. Clair River (Michigan, USA) and the lower Connecticut River (New England, USA) was restricted to the thermal refugia associated with power plant discharges during cold winter periods (French and Schlösser 1991; Morgan et al. 2003). Asian clams are also limited by high temperature extremes; massive die-­‐offs were observed during a European heat wave in 2003 in the Rhine River with water temperatures in excess of 28°C (Westermann and Wendling 2003; Cooper et al. 2005; Morgan et al. 2003). While water temperature has not limited the establishment of Asian clams in Lake Tahoe, it impacts reproduction and growth rate (Denton et al. 2012). Asian clams are hermaphroditic, with adults able to carry eggs for the entirety of their life (Kraemer and Galloway 1986). Most published accounts of Asian clam reproduction indicate bivoltine (i.e., two broods per year) spawning—beginning with the release of D-­‐shaped juveniles in the spring continuing into the summer, and once again in late summer continuing into the fall (McMahon and Bogan 2001; Sousa et al. 2008). Spermatogenesis and fertilization generally begin when temperatures rise in spring and exceed 10 and 15°C, respectively (Rajagopal et al. 2000; Cataldo et al. 2001) with upper thermal limits of 30°C observed (Aldridge and McMahon 1978). Thermal extremes observed in the mid-­‐summer period likely cease spawning of Asian clam individuals, and hinder their metabolic and reproductive rates, leading to the observed bivoltine spawning patterns (Gillooly et al. 2001; Enquist et al. 2003). To assess the relationship between Asian clam population density and thermal structure observed in Lake Tahoe, temperature collections occurred continuously at two locations in the 12
Tahoe nearshore (Marla Bay and Lakeside, 5 m) and monthly in the water column from 0-­‐80 m depths. Temperature samples were continuously collected at 5 m water depth using iButton® temperature loggers (Model #DS1922L, accuracy of ±0.5°C). Replicate loggers (n = 2 at each site) were weighted to the lake bottom and downloaded every six months. Water column temperatures were measured at 10 m intervals from 0-­‐80 m water depth monthly from 2008-­‐
2009. Degree day representing Asian clam reproductive period were also calculated for each depth in Lake Tahoe based on the number of days in which the mean daily water temperature was ≥16°C. Surficial Sediment total organic carbon and particulate organic matter Total organic carbon (TOC) and particulate organic matter (POM) were also collected in sediment samples. Water collected from the water-­‐substrate interface was processed to measure total organic carbon (mg TOC per 1 L water) with the elemental analyzer, Shimadzu TNPC-­‐4110C. To measure particulate organic matter (POM), up to one-­‐quarter inch of sediment substrate was obtained from the surface of the sample collected by the Ponar grab obtained for invertebrate collections. Samples were dried at 90°C for 48 hours, weighed, combusted at 475°C for 8 hours and reweighed. POM is expressed as the amount of organic matter ashed off during the combustion, calculated as the mean of the difference between pre-­‐ and post-­‐
combustion weights (mg POM per mg substrate). Regression analysis to determine the relationship between Asian clam density, distribution and environmental characteristics The complexities of most phenomena often require the collection of simultaneous observations of many different variables to clarify or understand patterns. Regression analysis is the statistical methodology for predicting values of one or more response (dependent) variables from a collection of predictor (independent) variable values. Multiple linear regression analysis (Johnson and Wichern 1999) was used to assess the relationship between Asian clam densities (as clams m-­‐2), Asian clam distribution in space (depth on surficial sediments and site location) and environmental characteristics as represented by the benthic and pelagic field surveys carried out from 2008 -­‐ 2009. The classical linear regression model states that Y is composed of a mean, which depends in a continuous manner on the z’s (independent variables) and a random error term, ε, which accounts for measurement error and the effects of other variables not explicitly considered in the model. The values of the predictor variables recorded from the experiment are treated as fixed. The error (and hence the response) is viewed as a random variable whose behavior is characterized by a set of distributional assumptions. Specifically, the linear regression model with a single response is utilized herein, and takes the form: 𝑌 = 𝛽! + 𝛽! 𝑧! + ⋯ + 𝛽! 𝑧! + 𝜀 Equation 4 Two methodologies were used for model selection: (1) stepwise regression (forwards and backwards), using the Akaike Information Criterion as a model selector, and (2) exhaustive search of all variable combinations using a branch and bound algorithm. To measure the 13
relative importance of each selected variable, bootstrap confidence intervals were determined to define uncertainties of the relative importance of selected environmental variables. All analyses were carried out in statistical package R (R Development Core Team 2012). The following variables were included in the regression analysis: (Dependent variable; Y) Asian clam density (2008 – 2009) for Marla Bay, Zephyr Cove and Nevada Beach, and (Independent variables; zr): (1) depth (m), (2) date, (3) year, (4) month, (5) degree days, (6) average monthly temperature, (7), chlorophyll a, (8) phaeophytin, (9) nitrate, (10) THP, (11) TP, and (12) sediment particle size. Results and Discussion Asian clam benthic surveys and distribution and size structure in surficial sediments Three transects at Marla Bay, Zephyr Cove and Nevada Beach were visited in October 2008, and then again in May and October 2009 from 0 to 40 m water depths to sample autumnal and spring populations of Asian clams on the surface of the bottom sediments. These transects were intended to observe changes in Asian clam population dynamics that may have occurred since the initial samplings of these areas in summer 2008 (Figure 4). Figure 4. Asian clam surficial sediment benthic survey results (Lake Tahoe, Summer 2008). All values represent clams m-­‐2. Purple circles indicate a grab sample that contained no Asian clam, blue triangle indicates 1 to 500, green diamond: 501 – 1500, red square: 1501 – 3500. 14
Of all three transect locations, Marla Bay consistently had the highest population densities with an average density (integrated over the 0 – 40 m water depth) of 1757 (SE±323) clams m-­‐2 in October 2008, 697 (SE±429) in the May 2009 sampling and 575 (SE±154) in October 2009 (Figure 5a, 5b, 5c). The greatest average density occurred at 3 meter water depth (3943 SE± 681 clams m-­‐2) and densities declined with depth, with lowest observed values occurring at 40 m water depth ranging from 94 to 599 clams m-­‐2 over the 2008 – 2009 sampling period. No clams were found below 40 m depth during this sampling. Nevada beach was sampled from 0 to 80 m water depths during the 2008 sampling, and from 0 -­‐ 40 m depth during the May and October 2009 sampling events. Average population density in the Nevada Beach transect, averaged over the 0 -­‐ 80 m water depth in October 2008 was 253 (SE±84) clams m-­‐2, with one live clam collected at 70 m water depth—the deepest ever recorded in any location. The May 2009 sampling had an average (0 -­‐ 40 m depth) density of 688 (SE±169, and in October 2009 (also 0 -­‐ 40 m depth) an average of 679 (SE±254) (Figure 6a, b, c). Asian clam population density maxima at Nevada Beach occurred at 20 m water depth in May 2009 with densities at this depth consistently high (similar to Marla Bay densities) over the entirety of the sampling period. Cold winter temperatures did not appear to have a large impact on Asian clam mortality in 2008-­‐2009, as population densities remained the same or increased between the October 2008 and May 2009 survey events. The increase of individuals observed in May 2009 was likely the result of the 2008 autumnal spawning event. In any case, the variation of the sample collections was large in the shallower depth zones (3 to 20 m) indicating the heterogeneity of Asian clam distribution in space. Asian clam population density at Zephyr Cove appeared to fluctuate in space (Figures7a, b). Average Asian clam density observed in October 2008 was 600 (SE±146) clams m-­‐2, with greatest densities (1830 SE±329) clams m-­‐2) observed at the 10 m water depth, in contrast to Marla Bay (5 m water depth) and Nevada Beach (20 m water depth). In May 2009 we found a considerably lower clam density at Zephyr Cove, with an average of 45 (SE±9) clams m-­‐2 and in October 2009, no clams were observed at any depth sampled in Zephyr Cove. A this time we do not know if this is a real trend or just normal interannual variability. Zephyr Cove maxima at both samples occurred at depths greater than 5 m water depth and samplings at all depths revealed great ranges in standard error, indicating the heterogeneity of distribution in space, similar to patterns observed in Marla Bay and Nevada Beach. Whether these fluctuations indicate that the population at Zephyr Cove is shifting in time or space is currently unknown and would require a much more intensive sampling effort over a multiple year time span. The size class structure of individuals in Marla Bay, Nevada Beach, and Zephyr Cove (Figure 8a, b, c) indicates that Asian clam are present at deeper water depths but the absences of high abundances suggests that there is potentially habitat dependences of the variability in mortality rates, fecundity or resource availability. This is to say, Asian clam individuals are either being transported by physical movement (e.g., entrainment in water currents or dispersal via individual movement) or by recruitment (e.g., individuals present at these depths are 15
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successfully reproducing), but the differences in population size and individual clam size indicate that there are environmental limitations (e.g., temperature, food availability) that preclude these populations and individuals from achieving biomasses observed in shallower zones. In Marla Bay, all size classes are represented fairly equally to 20 m water depth, with larger size classes reducing below that water depth. At depths greater than 20 m in Marla Bay, individuals in October 2008 does not exceed 10 mm in shell length, but in May and October 2009 individuals with shell lengths greater than 15 mm are observed. The same pattern is observed at Nevada Beach, with most individuals absent below the 20 m water depth, with an exception of a few small size classes being represented at the 40 m water depth in May and October 2009. The presence of all size classes was recorded in October 2008 in Zephyr Cove, however, none of the adult size classes (> 10 mm shell length) were observed in May 2009, and no adults or juveniles observed in October 2009. THE REST OF THIS PAGE HAS BEEN LEFT BLANK 17
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Figure 7. Zephyr Cove Asian clam average population density (±SE) within surficial sediments collected by Ponar grabs in a) October 2008 and b) in May 2009. Zero clams were recovered in the October 2009 sampling. Samples were collected from 0-­‐40 m water depth. 19
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09 in a) Marla Bay, b) Nevada Beach, and c) Zephyr Cove. Note one clam was collected from Marla Bay from 70 m and no Asian clams were present at 0-­‐40 m in October 2009. 20
Clam distribution within the sediment column Utilizing sediment cores (n=39) to understand the distribution of clams within the sediments, we sampled populations from Marla Bay and Lakeside. Densities from our cores were significantly lower than the densities observed in the surficial sediment grabs, ranging from 0 to 252 m-­‐2. None the less, we interpret the densities from the cores but suggest that most of the clams are distributed in the top of the sediment column but can occur at depth too (Figure 9). 84% of observed Asian clams were found in the top 6 cm of sediment, and remaining clams were found to a maximum depth of 16 cm in the Marla Bay site core samples. The greatest abundance of clams were found at 4 cm sediment depth; Figure 9 shows the size distribution of clams found in the sediment cores. The smallest clams (1-­‐5 mm) were the most common: 36.3% of all measured clams belonged to this size class followed by 32.5% (10-­‐15 mm), 25% (5-­‐
10 mm), and 6.25% (>15 mm). Clams (1-­‐5 mm) composed 50% and 58.6% of the clams in the 2 and 4 cm depths respectively. Only one clam of this size was found below 6 cm. The largest clams (>15cm) were exclusively found in the top 6 cm. Midrange clams, both 5-­‐10 mm and 10-­‐
15 mm, were found at all depths with clams, except 12 cm which only had clams 10-­‐15mm. Figure 9. Depth distribution of Asian clam size classes (as represented by anterior-­‐posterior shell length) by sediment strata (in 2 cm increments) in Marla Bay, Lake Tahoe. Each strata represents the proportion of size class averaged over 39 hand core samples. Surficial sediment particle size distribution and pore water In general, most of the benthic habitat in the nearshore zone of Lake Tahoe is composed of sandy substrate (Herold et al. 2007). Sediment particle size analysis from the five sites considered here shows a mildly varied distribution of sediment type, with most sediment 21
substrates comprised of sandy to coarse sand particles (Figures 10a, b, c, d; Table 1). Of the three sites sampled that had established Asian clam populations at the time of this study (Marla Bay, Lakeside, Nevada Beach; note that this study was done prior to the discovery of Asian clam in Emerald Bay), all were sandy or coarse sandy substrates (Figure 10a, b, c). Sediments outside of the Lakeside Marina had the greatest abundance of fine sand particles, with a mean sediment particle size (M0) of 0.25 mm (Table 1). In contrast, both Marla Bay and Nevada Beach sediments were dominated by larger particle sizes, considered as sand or course sand. Marla Bay had the greatest M0 of all sites sampled at 1.18 mm, with Nevada Beach also having course sand substrate with an M0 of 0.5. Of sediments sampled in sites without Asian clam presence (Crystal Bay and Emerald Bay1), both substrates were also characterized as silt-­‐fine sand to coarse sand, and considered as appropriate habitat for Asian clam (McMahon and Bogan 2001). Emerald Bay had the lowest M0 of all sites selected at 0.125 mm. Average ammonium concentrations in the water column in Marla Bay ranged from 4.5 to 7.3 ug/L, with the greatest concentrations (7.2 ±1.7 ug/L) observed at 1 m water depth (Figure 11a). Sediment porewater had greater ammonium concentrations (14.8 ± 3.4 ug/L) than any of the samples collected in the water column. Soluble reactive phosphorus (SRP) was very similar throughout the water column ranging from 1 – 1.2 with a standard error of 0.2 indicating no significant difference between samples (Figure 11b). Similar to the ammonium profile, SRP in the sediment porewaters was greater than in the overlaying waters at 5.6 ± 3.1 µg/L. Dissolved phosphorus (DP) was more variable than SRP with concentrations ranging from 11.9 to 19.7 in the water column (1 – 4 m depths) (Figure 11c). The greatest concentration of DP in the water column was observed at 2 m (19.7 ± 0.4 µg/L). Sediment porewater concentrations of DP were almost twice as high as any water column concentrations (32.4 ± 9.3 µg/L). Water column and sediment pore water nutrient concentrations Water column nutrient concentrations in Lake Tahoe were explored on two scales: a one-­‐time snapshot, high resolution sampling from 0 – 5 m water depth in Marla Bay, including sediment porewaters, and monthly sampling from 0 – 80 m water depth from 2008 – 2009. Analytes included in the high resolution sampling in Marla Bay were ammonium (NH4+), soluble reactive phosphorus (SRP), and dissolved phosphorus (DP). Monthly samplings of nitrogen and phosphorus in the open water column from 0 – 80 m depth had similar trends over the 2008-­‐
2009 period. Here we will consider average values estimated during the months of Asian clam samplings to correlate with environmental conditions considered at the time of the Asian clam population sampling. In October 2008, average nitrate-­‐nitrogen concentrations from 0 – 60 m water depth were 1.4 (SE± 0.1 µg/L). At 70 and 80 m water depths, there were significant increases in nitrogen concentrations, 14.7 and 19.1 µg/L respectively. In May 2009, nitrogen concentrations from 0 – 60 m water depth were almost five times as high as those measured in 1
At the time of this study, Asian clams were not yet discovered in Emerald Bay. As of 2010, low density populations
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Figure 11. Nutrient concentrations (ug/L) in Marla Bay from 0 – 5 m depth for a) ammonium (NH4), b) soluble reactive phosphorus (SRP), and c) dissolved phosphorus (DP). 0 m depth indicates surface sample and 5 m depth indicates sediment porewater concentrations. 24
October 2009, with an average of 5.7 (SE±0.3 µg/L). Similarly, there was an increase in nitrogen concentrations at 70 and 80 m with average values of 7.4 and 10.3 µg/L. In October 2009, nitrogen conditions were similar to those observed in October 2008, with an average from surface to 60 m of 1.6 µg/L (SE±0.1). Phosphorus concentrations amongst all three sampling times considered (October 2008, May 2009 and October 2009) were similar over all seasons. Average concentrations over the entire 0 – 80 m water column depth were 2.2 µg/L (SE±0.3), 1.6 µg/L (SE±0.1) and 1.4 µg/L (SE±0.1) in Oct-­‐08, May-­‐09 and Oct 09 respectively. The increased variability in October 08 and October 09 are due to observed increases in phosphorus concentrations at 70 – 80 m which were approximately twice as high as the water column average. Water column fluorescence, chlorophyll a and pheophytin concentrations In October 2008, chlorophyll a and pheophytin concentrations had average concentrations of 0.6 (SE±0.1) and 0.3 (SE±0.1) over the water column, but with maxima occurring at 60 m (chl a: 1.4, pheophytin: 0.9). In October 2009, average chl a was estimated at 0.7 (SE±0.1) and average phaeophytin concentrations were 0.3 (SE±0.1)—also with maxima occurring at 70 m. The October 2009 sampling was completely similar to the October 2008 results, with averages of 0.6 (SE±0.1) and 0.2 (SE±0.1) for chl a and phaeophytin, respectively. Photosynthetically active radiation, ultraviolet light, and dissolved organic carbon The information presented can be found in Tucker et al. 2010. The 1% UVR attenuation depths, that is, the depth where 320-­‐nm UVR reaches 1% of surface irradiance, show the wide range of UVR transparency of nearshore sites in Lake Tahoe (Table 2). UVR (320 nm) transparency of the near shore sites was strongly dependent upon DOC (Kd,320nm = [2.57*DOC2.53]; R2 = 0.81). However, a model that included both DOC and chl a (Kd,320nm = [1.95* DOC3.01] + (0.02*chl a]) was the best predictor of UVR attenuation (R2 = 0.98) for the sites sampled (likelihood ratio chi-­‐
square = 11.2, df = 1,P = 0.0008). Site Z1% 320 nm (m) DOC (mg/L) Chl a Tahoe Keys 1 0.4 1.77 2.47 Tahoe Keys 2 1.3 1.24 12.20 Emerald Bay 1 8.6 0.66 0.95 Zephyr Cove 30.3 0.51 0.52 Marla Bay 18.6 0.58 0.58 Sand Harbor 28.8 0.53 0.32 Crystal Bay 14.0 0.53 1.81 Tahoe City 1.1 1.0 144.70 McKinney Bay 17.8 0.58 0.42 Table 2. Attenuation depths, Z, for 320-­‐nm ultraviolet radiation (UVR) at nine sites in Lake Tahoe. Z1%320nm, is the depth where 320 nm UVR reaches 1% of surface irradiance. Also shown 25
are dissolved organic carbon (DOC) and chlorophyll a values for each of the nearshore sample sites (Results from Tucker et al. 2010). Influence of temperature on potential growth We found that nearshore temperatures at the 5 m water depth in Lake Tahoe are above 10°C from early May to November and above 15°C between late May through early September (Figure 12) which is similar to previous findings (Ngai 2008). Maximum temperatures in the Tahoe nearshore do not exceed 25°C, which is well below the upper thermal limit (>30°C+) for Asian clam reproduction to occur. Given this temperature setting, the reproductive period for Asian clams in Lake Tahoe was estimated to occur from late spring through late autumn with spermatogenesis occurring at 10°C in May, fertilization in June at 14-­‐15°C and juvenile release at 16-­‐18°C in July – October (Denton 2012). As a result of this thermal structure, the number of reproductive degree days (>16°C) at the 5 m water depth is estimated to be 96, according to measurements taken in 2009. As expected, the number of degree days decreases with depth. Degree day estimates based on the 2009 season were 103 d at 3 m, 96 d at both 5 and 10 m water depth, 73 d at 20 m, 8 at 30 m and 0 at 40 m and below. The relationship between number of degree days and average Asian clam density is shown in Figure 13. The thermal structure observed in Lake Tahoe suggests that Asian clams recruitment is likely limited by temperature and food (see below), limited below 40 m in Lake Tahoe, however there are observed juveniles and adults below this depth suggesting potential for advective transport mechanisms. If nearshore temperatures warm as a result of global changes as they are predicted to do, then the reproductive period for clams could change dramatically over time (Wittman et al, 2013, Ngai et. al. 2013). 30
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Figure 12. Temperature recorded at the Marla Bay and Lakeside during 2009. Marla Bay is represented by the black line and Lakeside by the gray line. In general, Lakeside temperatures are slightly warmer during the summer period and Marla Bay temperatures are slightly warmer during the autumn period of 2009. Figure 13. Asian clam density as a function of the number of days with temperatures >16°C in Lake Tahoe. Each point on the graphic represents the mean abundance for 3 ponar grab samples collected from 2–50 m water depths in Lake Tahoe. Multiple linear regression analysis to determine the relationship between Asian clam population density, distribution, and environmental variables Model selection based on Akaike Information Criterion (AIC) suggested that the most parsimonious model included sediment particle size and number of degree days. These variables explain 30% of the variation observed in Asian clam establishment in Lake Tahoe (R2 = 0.30, F = 18.52, p<<<0.001, df = 87). However, using the change in AIC to evaluate performance: another model that included sediment particle size, degree days and year had a difference in AIC of 0.33, and another model that included sediment particle size, degree days and phaeophytin as explanatory variables had a difference of AIC of 1.89. A change of AIC values with a value < 2 indicate that model performance is not significantly different. Thus, based on the principle of parsimony, our model selection will refer to the two predictors mentioned above. Normalizing the metrics of the two explanatory variables, the relative importance of each is 0.88 (CI: 0.6383, 0.9937) for degree days and 0.12 (CI: 0.0063, 0.3617) for sediment particle size based on bootstrapping estimation of confidence intervals (CI). This is to say that when the model terms are standardized to consider only the selected independent variables (i.e., degree days and sediment particle size) degree days explains to 88% of the 27
standardized model output and sediment particle size contributes to 12% of this model output. As stated above, the overall model only explains 30% of the variability of Asian clam density, as indicated above by the R2 value-­‐-­‐but the standardized relative importance indicates how these two variables contribute to this explanatory power. Estimated coefficients are the following: intercept: -­‐199.59 (CI: -­‐506, 107; p = 0.19), degree day: 9.35 (CI: 6, 13; p<<<0.001) and sediment particle size: 393.10 (CI: 21, 766; p = 0.038) giving the following model structure: YAsian clam density = -­‐199 + 9*DegDays + 393*M0 + ε Equation 5 Where Y, indicates Asian clam density as predicted by the number of Degree Days (DegDays) and the sediment type (M) and an error term (ε). In summary, the variation of Asian clam populations densities can be explained by the number of degree days (positively associated with warmer water temperatures for longer periods of time) and sediment particle sizes (positively associated with larger, coarser sandy sediment types). THE REST OF THIS PAGE HAS BEEN LEFT BLANK 28
B. What is the life history and growth of clams in oligotrophic Lake Tahoe? Background of reproductive cycles in other lakes, general attributes of Asian clam life history, and population viability analysis The invasion of Asian clam in Lake Tahoe is unique due to the potential impact of the lake’s cold temperatures, high elevation, oligotrophic status, and food web structure on Asian clam life history strategies. Most published studies of Asian clam occurrence have recounted population growth, reproduction and dispersal in warmer climates (Aldridge and McMahon 1978), lower elevations (Sousa 2008) or eutrophic systems (McMahon 2002, Sousa et al. 2008) (Table 3). In Lake Tahoe, Asian clam are observed to be smaller than in other systems (with a maximum shell size of 25 mm), yet have a comparable vertical distribution to other systems such as Lake Mead (> 70 m water depth; Wittmann et al. 2010) , and some of the highest population densities recorded in any system. Understanding demographic processes observed in Lake Tahoe will improve risk assessment of Asian clam potential for spread and impact to the ecosystem, and can improve management of this species in the Tahoe Basin and in similar environments. In aquatic systems outside the Tahoe basin, Asian clam have rapid growth and dispersal rates, are hermaphroditic, and have variable frequencies of reproductive events during the year (McMahon 2002). A majority of studies concluded that this species reproduces twice a year (Sousa 2008): one occurring in the spring and continuing during the summer and the other beginning in late summer and continuing through the fall. However, some studies found only one reproductive event, while in others were found with differences among years even at the same locations (Doherty et al. 1987). Asian clam grow rapidly, in part due to their high filtration and assimilation rates (McMahon 2002). Most of its allocation of energy is for growth and reproduction with a small portion is devoted to respiration (McMahon 2002). In other systems, Asian clam has also been observed to have high fecundity but a low juvenile survivorship and a high mortality rate throughout life span. This low adult survivorship leads to populations dominated by high proportions of juveniles (McMahon 2000, 2002). In some ecosystems this population domination by immature juveniles is not so effective and the presence of adults in high abundance and having large sizes has been reported (Boltovskoy et al. 1997, Sousa et al. 2008). Reproduction of Asian clam can be prolific as a result of hermaphroditism, rapid reproductive maturity, and variable larval incubation periods as short as 6 days, normally upward to 2 wk or as lengthy as 60 days in a wide range of environmental conditions (King et al. 1986, Kraemer & Galloway 1986, McMahon 2000, Rajagopal et al. 2000). Asian clam eggs are held in the inner demibranches of the ctenidia (gills) after release from gonads, then fertilized, and embryos are brooded in the same structure. This may result in an annual fecundity rate of as many as 68,000 juveniles per individual (Aldridge & McMahon 1978, McMahon 2002). Temperature initiates multiple stages of reproduction, and Asian clams generally have a bivoltine reproductive cycle in response to temperature regimes in rivers, lakes, and reservoirs (Aldridge & McMahon 1978, Kennedy & Van Huekelem 1985, Rajagopal et al. 2000, Mouthon & Parghentanian 2004). An initial spawn commonly occurs during the spring after threshold temperatures have been reached (at least 16–18 C for at least 10 degree-­‐days); however, once temperatures exceed 27–28 °C, reproductive output is restricted (McMahon 2000, Mouthon 29
2001, Mouthon 2001b). A subsequent, weaker spawn may occur after a return to lower temperatures (Aldridge & McMahon 1978, Kennedy and VanHuekelem 1985, Rajagopal et al. 2000, Mouthon & Parghentanian 2004). Although temperature is the primary cue for initiation of reproduction, food availability is also important for embryo development and successful brooding (Doherty et al. 1987, Mouthon 2001b). Overall food availability has been found to enhance gonad development and fecundity, and increases both the brood size and individual size of developing embryos (Beekey & Karlson, 2003). To support growth and reproduction, two feeding strategies are used: suspension feeding from the water column and deposit feeding in the substrate. Suspension feeding rates of C. fluminea are variable but can be high, between 300–2,500 L/h (McMahon & Bogan 2001). In the absence of suspended food, such as that seen in oligotrophic ecosystems, C. fluminea can ingest sediment particulate organic matter (sPOM) through deposit feeding (Reid et al. 1992), consuming upward of 50 mg/day and doubling growth rates (McMahon & Bogan 2001). The objective of this study was to investigate the factors that influence the reproductive efforts (timing and overall fecundity) of Asian clam in Lake Tahoe. Utilizing a combination of field experiments, dissections of clams, and information gathered from a literature review, we tested the following hypotheses: (1) temperature would have the greatest influence on the timing of reproductive initiation; (2) food availability, represented by a coarse proxy of total organic carbon (TOC) and sPOM, would influence overall reproductive effort; and (3) reproductive efforts would be similar in both shallow and deeper populations, resulting in a source of veligers for populating the nearshore environment. Understanding Asian clam reproductive and growth dynamics in environmentally heterogeneous, patches in Lake Tahoe will allow for the prediction of future growth and reproductive potential of this invasive clam in Lake Tahoe. One well-­‐known tool for assessing the future population status of a species is a quantitative modeling framework called population viability analysis, or PVA (Morris and Doak 2002). PVA models intend to use field collected data and link the results to models of population growth and/or decline. PVAs have typically been used by governments, agencies, scientists and conservation groups to assess the extinction risk of threatened or endangered populations such as Yellowstone grizzly bears or Loggerhead sea turtles, but also for marine bivalves such as the blue mussel (Morris and Doak 2002). PVAs can be used to guide management by (1) identifying key life stages or demographic processes as management targets in an effort to reduce or increase population abundances, (2) determining how large a reserve or easement needs to be in order to gain a desired population level for a species, (3) determining how many individuals to add or remove to a population to get the desired abundance, or (4) setting quotas or limits on harvest (or take) from populations that are compatible with its continued existence and/or reduction (e.g., invasive species). The general steps of building a population viability analysis are founded in measurements of three types of demographic processes called vital rates. Vital rates include the probability of survival during the desired period of study (i.e., 1 year, multiple decades, etc.), the probability that given survival, it transitions from its current life stage to another (i.e., from a juvenile to an adult), and the probability that an individual’s life stage influences the number of offspring it will produce (i.e., particular age classes may be more fecund than others). Using these three vital rates, a projection matrix is built to predict future population growth estimates. The construction of a projection matrix model follows three general procedures: 30
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Conduct a detailed demographic study of a representative set of marked individuals, measuring their survival, state, and rate of reproduction. 2.
Use the demographic data described in (1) to estimate the vital rates for each size class in each year. 3.
Use the class-­‐specific vital rate estimates to build a deterministic or stochastic projection matrix mode and estimate a parameter, λ, which is representative of a population’s growth rate. If λ > 1, long term population growth is positive, if λ < 1, growth rate is negative and if λ = 0 population growth rates are static. We use two years (2008 – 2009) of Lake Tahoe field and laboratory experimental data to inform a PVA to assess population growth and viability of this species under a high and low population density scenarios. We will use measurements of Asian clam fecundity (through late stage veliger and egg counts), Asian clam growth (through a mark recapture experiment in the field), and field sampling of population structure and abundance through benthic survey and count data to estimate the long term population growth patterns of this species in an oligotrophic, subalpine lake. In addition, we will present the results of Asian clam sediment preference experiments for to determine likelihood of expansion to new habitats in Lake Tahoe, based on sediment type. Population Structure, Fecundity, and organic matter as drivers of production Methods In order to collect information to population the population viability model (e.g., fecundity rates and egg counts per individuals) and to assess the population structure of Asian clams in Lake Tahoe, four sites with established Asian clam populations were sampled: Lakeside, Marla Bay, and Nevada Beach each at a depth of 5 m, and Nevada Beach at a depth of 20 m (hereafter referred to as LS5, MB5, NV5, and NV20). Asian clams were collected using using a petite Ponar grab (area, 225 cm2) biweekly from May through August (late spring to summer) and monthly from September through November (fall) 2010. Lake water was collected near the water–
substrate interface using a Van Dorn sampler and measured for in situ temperature using a hobbyist digital thermometer (Coralife ESU Digital Thermometer). In situ point measurements for temperature were validated against a continuous temperature data logger that indicated a clear relationship among the measurements to describe seasonal patterns in temperature (Denton, unpubl. data). TOC in the overlying lake water was analyzed with an elemental analyzer (Shimadzu TNPC-­‐4110C). sPOM was gathered from a thin scraping of the surface sediment (≤1 cm in depth) obtained from the Petite Ponar sample, and measured as loss on ignition (Froelich 1980). Environmental conditions were analyzed by a 1-­‐way ANOVA for temperature and TOC for site and date independently, and 2-­‐way ANOVA analyzed sPOM by site by date, and a pairwise difference was determined with Tukey’s HSD post hoc analysis. All statistical analyses were performed using SAS 9.2 (SAS Institute, Inc., Cary, NC) and Minitab 15.1 (Minitab, Inc., State College, PA). 31
All Asian clam samples were held in 18-­‐L field buckets with sediment and lake water, stored at 10°C, and processed in the laboratory within 24 h of collection. Samples were elutriated in the laboratory and sieved through 90-­‐mm mesh to retain the smallest individual clams and to calculate abundance (measured as clams per square meter) for each sampling period and location. All grabs were combined into 1 sample per site per date; therefore, variations in dates by individual sites were not determined. Reproductive Effort To quantify eggs and developed fertilized larval forms (hereafter referred to as veligers), we dissected the ctenidia (gill structures) of approximately 40 clams (shell length, 13 ± 1 mm) per site across sampling dates. Clams between 11 mm and 19 mm were dissected occasionally when the target size class was not met completely. Clams were measured for shell length with digital calipers to the nearest 0.01 mm prior to dissection. Ctenidia were squash mounted and examined under 100X magnification light microscopy (Morton 1977, Britton & Morton 1982). Developmental stages were determined based on the descriptions from Kraemer & Galloway (1986). Results and Discussion Environmental Conditions At all sites, temperatures were less than 8.0°C on May 11, with the greatest increase in temperature from June 16–28 (Figure 14). Seasonal high temperatures were recorded at each site on July 20. A temporary decrease in temperatures on August 30 was associated with a cold front that passed through the Tahoe basin at that time. Temperatures were significantly different over dates (P < 0.0001) but not sites (P = 0.659). TOC concentrations were not significantly different among sites (P = 0.549). Mean concentrations (±SE) across all dates (n = 10) at each site were 10.7 ± 0.5 mg/L (LS5), 10.7 ± 0.4 mg/L (MB5), 10.9 ± 0.5 mg/L (NV5), and 10.7 ± 0.5 mg/L (NV20). There was a significant site-­‐by-­‐date interaction in sPOM (P < 0.0001). A Tukey post hoc analysis determined that LS5 (6.8 ± 3.3mg/mg) and NV20 (6.1 ± 4.4 mg/mg) had greater concentrations of sPOM than MB5 (4.6 ± 2.9 mg/mg) and NV5 (3.6 ± 1.1 mg/mg) during the season (Figure 15). Reproductive Activity A total of 1,875 clams were dissected to determine their reproductive status and activity throughout the course of the sampling period. The mean shell length at each site during the entire sampling period was 13.68 ± 1.3mm (LS5, n=461), 13.33 ± 0.7 mm (MB5, n = 479), 13.91 ± 1.0 mm (NV5, n = 478), and 13.74 ± 1.2 mm (NV20, n = 457). Eggs were present in the demibranches on all sampling dates from May 11 to November 5 (Figure 16). Egg abundances observed had a significant site-­‐by-­‐date interaction (P < 0.0001), and a Tukey post hoc analysis determined that the greatest abundance occurred on August 30. Veligers were detected in the middle to end of summer and occurred in low abundance on August 16, and were in high 32
abundance on August 30 and September 13. These sampling dates were +27, +41, and +55 days after the critical temperature threshold needed to initiate a spawning of brooding veligers (King et al. 1986, Kraemer & Galloway 1986). There was a significant site-­‐by-­‐date interaction of brooding veliger abundance, and a Tukey post hoc analysis showed that August 30, September Table 3: Comparisons of C. fluminea reproductive characteristics from populations in ecosystems of varying limnological structure. Chl-­‐a Depth # of Location Cycle Reference (m) Veligers (µg/L) Lake Arlington, 1-­‐5 23.32 * Bivoltine. Spring: Aldridge and TX (reservoir) Spring spawn @ 19°C, 588/c/d McMahon, 1978 terminate late July with Fall: temps >32°C. 735/c/d Fall spawn when temps <32°C, terminate when temps <18°C. Potomac River, Shallow, 100-­‐300* Bivoltine. Kennedy and MD inshore Spring (67—85%) and Fall Van Huekelem, (10—40%) of population 1985 New River, VA Univoltine. High Doherty Continuously from late ~1800/c/d et al., 1987 spring to mid-­‐autumn. on June 26. High spring spawning for 5 weeks, reduction in spawning ~4 weeks, increased spawning for 8 weeks August – October. Post-­‐October, veliger release ceased. Rhine River, Lower Bivoltine. Rajagopal et al., Netherlands Rhine 5-­‐
May when temps >15°C. 2000 70 First generation can River Lek: reproduce by September. 5 -­‐39 No spawning after October when temps <15°C. Villebois 0.5 – 1.5 <3 Univoltine. Mouthon, 2001 reservoir, July through Rhône River, September/October France Canal of 2.25 – 2.5 Loire Bivoltine. Mouthon and Roanne and March=90 June, then August. Parghentanain, Loire Lateral Oct<5 2004 Canal, France Roanne March=80 Oct<5 *Values not reported in cited study but obtained from another source. 33
Figure 14. Temperatures at the water-­‐substrate interface by sampling date and site. Documented threshold temperatures required for reproduction based on the literature (McMahon 2000, Mouthon 2001a, b) are noted with the vertical bars indicating when temperatures were recorded. A, the onset of spermatogenesis, B, fertilization occurs, C, initial release of veligers. Figure 15. Values of Sediment Particulate Organic Material (sPOM) within 2 cm of the sediment surface. 34
13, and October 8 had the greatest abundance of veligers present, and the veliger abundance at shallow locations was significantly greater than NV20 (P < 0.0001). Across all three shallow sites, there were similar levels of reproductive effort, with a mean veliger abundance per clam (±SE) of 10 ± 2 (n = 603), with ranges of 286 ± 28 (n = 25 for clams with ≥100 veligers) and 20± 2 (n = 78 for clams with <100 veligers), and 498 clams had no veligers present in samples from mid-­‐August through early November. NV20 had a mean abundance of 3 ± 1 veligers across 4 clams, with 196 clams having no veligers present in samples during the same period. Population Structure Overall population abundance was significantly different by site over all sampling dates (P = 0.0013), with abundance at NV20 (2,541 ± 291 clams/m2) significantly greater than the shallow sites (Figure 17). The distribution of Asian clam was heterogeneous along the bottom at each site. Across sites for all sampling dates, there was a significant difference in the number of grabs obtained to meet the needs of dissection (P= 0.0014), with LS5 requiring the greatest number of samples over the dates (9 ± 3 grabs per date), MB5 and NV5 requiring fewer but nearly equal numbers of grabs (7 ± 2 and 7 ± 1 grabs per date, respectively), and NV20 requiring the fewest (5 ± 2 grabs per date, n = 12; SD is noted because SE values were less than 1). Figure 16. Mean values (±SE) for eggs (no shading) and veligers (shading), and percentage of populations with eggs (solid line) and veligers (dotted line). 35
Size class distribution of Asian clam by site suggests differences in population structure (Figure 17). Size class distributions in LS5 were variable, but no one size class (or group of size classes) dominated the population structure throughout the sampling season. Size classes between 13 mm and 17 mm represented a majority of the populations in MB5, with clams occasionally reaching a shell length of 22 mm. Shell lengths of <4 mm were absent from these samplings. At NV5, the <4-­‐mm size class was present in all samplings with very low presence during June 16 and November 5. The 13–17 mm-­‐size class was large throughout the sampling dates, and clams disappeared from the population after 22 mm. The <4-­‐mm size class at NV20 was present on June 16 and September 13. For a majority of the other samplings, this size class was completely absent, with a minimal presence on August 2, October 8, and November 5. As in the other locations, the largest size class was the 13–17-­‐mm group. Asian clam in Lake Tahoe are univoltine, with reproduction in the late summer and low abundance of brooding veligers. There was a longer than expected delay between threshold temperatures for required reproduction based on previously published literature and empirical observations of brooding veligers made during dissections. Given that oogenesis occurs independent of temperature (Kraemer & Galloway 1986), we expected eggs to be present during all dissections. Because spermatogenesis and fertilization require minimum temperature thresholds to be met (10°C and 14°C, respectively), brooding veligers should not have been present until temperatures were at least 14 C for 10 consecutive degree-­‐days (Kraemer & Galloway 1986). Temperatures across all shallow sampling sites reached this threshold by July 20, with a mean of 19.7 ± 0.4°C. A typical cycle of initial fertilization, larval maturity, to release of veligers is 6–14 days (Kraemer & Galloway 1986), with release occurring at least 16.0°C. In other systems, Asian clam are observed to be bivoltine, with the first spawn occurring in late spring to early summer, and resuming in late summer. This pattern has been attributed to metabolic declines resulting from temperature increases greater than 27.5C (Aldridge & McMahon 1978, Mouthon 2001a). When spawning did occur in Lake Tahoe after a 4-­‐wk delay, the overall abundance of veligers observed in the shallow sites (10 ± 2 veligers per clam) was much lower than the veliger abundance observed in more productive reservoir or riverine ecosystems. In these ecosystems, veliger reproductive efforts range from 588 to 735/clams per day in spring and fall (Aldridge & McMahon 1978) and 1,800 to 1,200/clams per day from late June and early October, respectively (Doherty et al. 1987). Recent studies have shown that Lake Tahoe’s surface waters are warming at a faster rate than ambient air temperatures (Schneider et al. 2009, Coats 2010). In the future, this increase in water temperatures may expand the spawning potential of Asian clam to an earlier initiation of reproductive development, and a longer fertilization and release period. It is unlikely, however, that a bivoltine spawning event will occur in Lake Tahoe because current temperature warming forecasts for the nearshore do not suggest an increase in temperature that would stop and reinitiate spawning, as found in warmer ecosystems. Alternatively, warming of the lake in the winter prior to the spawning cycle could enhance the reproductive success of Asian clam (Weitere et al. 2009). In other systems, food availability has been observed to be a significant contributor to spawning events of Asian clam to meet the energetic demand of brooding (Mouthon 2001b). Asian clam brood veligers within the inner demibranches of the gills, which have secretory cells believed to provide nutrients to developing embryos (Britton & Morton 1982, 36
Doherty et al. 1987). Although other studies reported chlorophyll a concentrations in systems with successful Asian clam populations ranging from 3 to 100 mg/L (Cohen et al. 1984, Mouthon 2001b, Mouthon & Parghentanian 2004), chlorophyll a concentrations in Lake Tahoe range from 0.5–1.5 mg/L (TERC 2010). It is possible that these low concentrations could limit Asian clam growth and could reduce nourishment for brooding embryos. TOC at the water–
substrate interface suggests similarly low food concentrations from this source. Although there were significant site-­‐by-­‐date differences for sPOM, overall reproductive effort was not significantly different among the sites, suggesting that variable concentrations of sPOM and TOC are not predictors of the fecundity of Asian clam in Lake Tahoe. Further investigation of food availability—in particular, food quality—is needed to understand its role in Lake Tahoe clam reproductive effort with respect to water temperature. In determining the similarities, if any, of reproductive effort between the shallow-­‐ and deep-­‐water populations, an interesting observation is the low count of veligers seen in dissections from NV20, the deeper water site. Veligers were found on only 1 sampling date (August 30), with just 13 veligers seen in 4 clams. However, this site has the highest overall abundance among sites, with significant increases in observed abundance in the late summer. Population size structure at this site indicated an increase in abundance is toward the larger size classes (>13 mm) rather than recruitment of juveniles (<4 mm; Fig. 17). This suggests that deep-­‐water populations are not reproductively active, and therefore are potentially a sink of clams rather than a source. If this is the case, clams would have had to be transported from the shallow depths to these deeper populations. Movement of clams to this deeper region may occur in 2 ways. One documented means of dispersal for Asian clam is via floatation. Prezant and Chalermwat (1984) found that clams up to 14 mm, when exposed to a current of 10–20 cm/sec, would push off the substrate with their foot while extending both siphons. They excrete a long mucus thread that allows them to be lifted and carried in the water column until the current subsides. This current is typically not found in the nearshore of lakes. Another possibility is that wind-­‐driven waves creating high-­‐
energy turbulence may transport clams from shallow depths to deeper locations. Redjah et al. (2010) found that the clam Mya arenaria, up to 20 mm, was displaced when subjected to turbulence in a level experimental flume with a high wave current flow. In addition, in a sandy substrate similar to the NV5 and NV20 sampling sites, St-­‐Onge and Miron (2007) found that between 40–90% of M. arenaria were eroded (transported) at stream velocities of 29–35 cm/s. With an approximate horizontal distance of 60 m between the 5-­‐m and 20-­‐m depth at Nevada Beach, an estimated slope of 18 deg, and documented populations of clams at 10mand 15m (unpublished samplings for 2008 and 2009), it is conceivable that high-­‐energy turbulence resulting from internal lake currents and other physical waves could transport both juvenile dispersers and adult clams along the slope to deeper depths. Throughout the 2010 sampling period, the juvenile size class (<4 mm) appeared sporadically across all sites and was probably a result of carryover from reproduction in 2009. Unlike other systems that show a pyramid-­‐shape size class population structure, with less than 4mm as the dominating the population (Hall 1984, Mouthon & Parghentanian 2004), the Lake Tahoe population contained more individuals in the 10–17 mm size classes, with a sharp decline in abundance of larger individuals in the range of 19–23 mm. Joy (1985) reported no shell growth for Asian clam for water temperatures between 0 C and 13.0 C. Given that newly 37
released veligers are 0.2 mm, and depending on the previous season’s release period, it is conceivable that the 2009 spawn would appear as a new size class the following midsummer 2010. Temperatures in this study were less than 13.0 C by November; therefore, juveniles spawned in the 2010 season would likely not experience shell growth until May 2011 or June 2011. Figure 17. Population structure of sampling sites across dates expressed as a percentage on the primary vertical axis. Abundance (number of clams per square meter) is represented by a line on the secondary vertical axis. 38
Asian clam in situ survival, mortality and growth experiments Methods To estimate survival, mortality and growth of Asian clams in a low and high Asian clam environment an in situ growth chambers experiment was carried out in Marla Bay and outside Lakeside Marina in Lake Tahoe from January 2010 through February 2011. At each location one chamber each was placed on the lake bottom at 5 m depth (Figure 18). The chamber dimensions were as follows: base diameter = 1.06 m, cylinder height = 0.95 m and the sides of the chamber were composed of ¼ inch mesh to permit the flow of lake water and to prevent predation from crayfish or other fish. Upon placement of the chamber, the bottom 4 – 6 inches was filled with benthic materials (sediments, algae, macroinvertebrates (including Asian clams)) collected from the area immediately adjacent to the chamber to simulate the surrounding benthic habitat. Figure 18. View from above of Asian clam growth chamber placed on the lake bottom at 5 m depth. Base diameter = 1.06 m, cylinder height = 0.95 m. At the onset of the experiment in January 2010, Asian clams (n = 40) ranging in sizes from 4 to 19 mm were marked with unique identifiers. Asian clams were equally grouped based on shell size class (mm): 5 – 8, 8 – 11, 11 – 14, and 14+. Shell length, or, the greatest anteroposterior dimension across the valves was measured with digital calipers to the nearest 0.01 mm, and placed in each chamber. Every 1 – 2 months until February 2011 the chambers were recovered from the lake bottom, marked clams were retrieved and if viable, were measured and placed back into the growth chamber and returned to the lake bottom. If marked individuals were deceased, mortality was recorded. Results and Discussion 39
0.09
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Mean annual shell production rates between Lakeside (0.0036 mm d-­‐1) and Marla Bay (0.0036 mm d-­‐1) were not significantly different from one another (t = -­‐0.4023, p = 0.34, df = 9). However, shell size class and seasonality had a large impact on observed shell production rates (Figures 19, 20). The smallest size class grouping (5 – 8 mm) showed the greatest mean shell production rates at both Marla Bay (0.06 ± 0.02 mm d-­‐1) and Lakeside (0.05 ± 0.02 mm d-­‐1) during the summer period (Jun – Aug). Shell production rates declined proportionately with increasing size as expected. Positive shell production at both sites was observed from May through early January at both sites, however shell production ceased during colder water temperature periods (5 – 13°C) from late January through late May, even though the clams were still viable. There was some evidence of adsorption (negative shell production), but none of the values indicated were outside the margin of error specified on caliper sets used in the field. Maximum shell production at both Lakeside and Marla Bay occurred in the June – August period when average water temperatures ranged from 14 – 17 C. After this period, peak temperatures reached 20 C and shell production reduced dramatically—likely in response to a shift of energy toward spawning. Marla Bay yielded a 45% annual mortality rate observed compared to 18% observed at Lakeside. Jan - Mar - Apr - May - Jun - Aug - Oct - 0
Mar
Apr May
Jun
Aug
Oct
Jan
-­‐1
Figure 19. Shell production rates (mm d ) by Asian clam size class (based on anterior shell length, in mm) from January 2010 – January 2011 in Marla Bay. Average monthly temperature (°C) is plotted on the secondary y axis. Error bars represent standard error. 40
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Figure 20. Shell production rates (mm d-­‐1) by Asian clam size class (based on anterior shell length, in mm) from January 2010 – January 2011 in Lakeside. Average monthly temperature (°C) is plotted on the secondary y axis. Error bars represent standard error. Population Viability Analysis (PVA) Methods The fecundity, growth, survival and mortality information, and population size class distribution information collected from field surveys described above (see section Benthic surveys and distribution in space on surficial sediment across the lake at established locations and within the sediment column) form the bases of the demographic study (PVA model) of Asian clams in Lake Tahoe. The following section outlines how these measurements were used to estimate vital rates for Asian clams in a high population density site (Marla Bay) and a low population density site (Lakeside). The results of the PVA present long-­‐term projections of Asian clam population growth in Lake Tahoe. The size class distribution of an annual population of Asian clam in Marla Bay and Lakeside were determined using seasonal samplings at both sites in 2010 (January, March, June, November). Six size class stages (in mm) were chosen to represent the population: 0-­‐1, 1 – 5, 5 – 8, 8 – 11, 11 – 14 and 14+. In general, size classes of individuals in bivalve populations are continuous, meaning that the length of the shell is not necessarily a representation of a discrete life stage. Because of this, we selected these six sizes classes based on abundances of size structures observed in Lake Tahoe. The 0 – 1 mm size class distribution was estimated using counts of late stage veligers as estimated in Section 1 of Objective 2 and multiplied by the number of reproductively viable (>10 mm) adults measured in the field. The rest of the population abundance was determined through the seasonal samplings. Asian clam fecundity, defined as potential reproductive capacity of an individual (or population) was estimated using 41
egg counts described in Section 1 of Objective 2 multiplied by the number of days over the annual period with temperatures high enough to support gametogenesis (>10 C). Growth and mortality information were collected from the growth chamber experiment described in Section 2 of Objective 2, where the probabilities of survival (proportion of individuals in size class i at the last census that are still alive in the current census), state transition (probability that a surviving individual undergoes a transition from its original size class to other potential size classes), or state stasis (remaining in the same size class during an annual period were estimated). Summaries of these data are presented for Marla Bay (Table 4) and Lakeside (Table 5). Site 1: Marla Bay Fecundity Density (eggs per Probability of Probability of number of Size class Percent of female per staying in size moving to next individuals (mm) Population year) class size class (m-­‐2) 0-­‐1 0.879 0 0.000 0.013 31535 1-­‐5 0.011 0 0.238 0.540 401 5-­‐8 0.014 0 0.000 0.666 491 8-­‐11 0.023 970 0.308 0.385 819 11-­‐14 0.039 2046 0.143 0.214 1404 14+ 0.034 2046 0.5 0.000 1220 Table 4. Vital rates for Asian clams in Marla Bay, Lake Tahoe Site 2: Lakeside Density Fecundity (eggs Probability of Probability of number of Size class Percent of per female per staying in size moving to next individuals (mm) Population year) class size class (m-­‐2) 0-­‐1 0.756 0 0.000 0.020 1389 1-­‐5 0.015 0 0.084 0.666 27 5-­‐8 0.010 0 0.1 0.600 18 8-­‐11 0.023 2904 0.083 0.833 43 11-­‐14 0.033 6122 0.385 0.462 62 14+ 0.163 6122 0.5 0.000 299 Table 5. Vital rates for Asian clams in Lakeside, Lake Tahoe Class specific vital rate information (Tables 4, 5) were then used to build a site specific projection matrix. In a projection matrix, the number of rows and columns in the matrix equal the number of classes into which the population has been divided (i.e., our six size classes). The first row of the matrix indicates the estimated fecundity for each size class. Each subsequent row in the matrix indicates the probability of state transition or state stasis. This matrix is called 42
a stage-­‐structured matrix because individuals that survive in a particular year do not necessarily have to transition to the next size class (i.e., can remain in their own size class over the transition period). Asian clam are modeled as a birth-­‐flow population with a post-­‐breeding census, meaning that the reproduction terms in the matrix represent the number of juveniles that each adult in the current census will contribute to the next census (Morris and Doak 2002). The projection matrix is iterated over a 10-­‐year period with annual updates of survival and growth probabilities and population size estimates. The model does not incorporate density dependence, i.e. Asian clams populations can grow at infinitum. Average population abundance and distribution determined from the 2010 survey in Lakeside (low density site) are used as the original population structure to initiate the model simulation period. Results are provided as the following. First we present the life cycle diagram for Asian clams in Marla Bay and Lakeside, which is a graphical representation of the transition states and contribution to population growth by size class. Second we present the modeled projection matrices derived for both populations (Marla Bay and Lakeside). Third, we present λ, or the annual population growth rate. If λ > 1, long term population growth is positive, if λ < 1, growth rate is negative and if λ = 0 population growth rates are static. And fourth, we present population size (millions of individuals) using estimates of average population as measured in Lakeside in 2010 as the original population size. Results and Discussion Asian clam populations in Lakeside and Marla Bay have different population structures and population growth rates. In both populations, the second size class (1 – 5 mm) is the stage with the greatest proportion of individuals contributing to the entire population as a result of growth and survival (Figure 21, 22). In both populations, individuals of this size class both transition to the next size class, as well as remain in the current size class over an annual period. However, in the next larger size class (5 – 8 mm), all individuals in Marla Bay transition to the larger size class in an annual period, whereas not all individuals in Lakeside do—indicating differences in growth (as estimated by shell production) rates. Projection matrices indicating the values of each transition probability of life cycle diagram are presented in Figures 21 and 22.. Both populations (Marla Bay and Lakeside) have positive population growth rates. The annual population growth rate, λ, for Marla Bay was estimated to be positive with a value of 1.8 and for Lakeside was estimated to be greater in Lakeside at 2.8. The larger λ at Lakeside in comparison to Marla Bay is a function of the increased fecundity observed at that site and a greater proportion of the population occupying the reproductively viable stage classes (i.e., > 10 mm). While density dependence is not included in this model, reduced fecundity rates in Marla Bay are likely a result of high population densities, lending towards a lower long-­‐term reproductive rate. Using population growth rates (λ = 2.8) estimated from the Lakeside site as a worst case scenario we modeled the population size of Asian clam in Lake Tahoe over a ten year period. We estimated that there are approximately 36 million m2 of suitable habitat (i.e., sandy substrate) in Lake Tahoe (Herold et al. 2007) and at present, approximately 20% of this habitat has established Asian clam populations (Forrest et al. 2012). We combined observed densities from Lakeside (Table 5), and extrapolated the model across all of the current invaded area of 43
𝐿!"
0
0
0
1182 2046 2046
0.013 0.238
0
0
0
0
0
0.54
0
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0
=
0
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0.666 0.308
0
0
0
0
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0.385 0.143
0
0
0
0
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0.214 0.5
Figure 21. Life cycle diagram for Asian clams observed in Marla Bay, 2010 – 2011. Numerals 1 – 6 indicate the size classes represented in the study: 0-­‐1, 1 – 5, 5 – 8, 8 – 11, 11 – 14, 14+ mm individuals. The size of the node indicates the stage structure (stage is correlated with size class) of the population. The width of the arrow is proportional to the transition probability. The back-­‐arrows indicate the proportion of individuals that remain in that size class. Projection matrix derived from field measurements of Marla Bay vital rates. The top row indicates fecundity (number of eggs per individual) for each size class and the subsequent rows indicate transition probabilities. 0
0
0 3887 6122 6122
0.02 0.084 0
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𝐿!" =
0
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0 0.833 0.385
0
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0
0
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0.462 0.5
Figure 22. Life cycle diagram for Asian clams observed in Lakeside, 2010 – 2011. 1 – 6 indicate the size classes represented in the study: 0-­‐1, 1 – 5, 5 – 8, 8 – 11, 11 – 14, 14+ mm individuals. The size of the node indicates the stage structure of the population. The width of the arrow is proportional to the transition probability. Projection matrix derived from field measurements Lakeside vital rates. The top row indicates fecundity (number of eggs per individual) for each size class and the subsequent rows indicate transition probabilities. 44
St
ag
e
e5
Sta
g
6
4
ge
Sta
3
ge
Sta
Sta
104
ge
2
106
ge
1
108
Sta
Population size
(Millions of individuals)
100
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Time step (years)
Figure 23. Population growth of clam stage structure using projections of population growth from Lakeside experiments. Dashed lines indicate life history stages contributing with measurable fecundity. Population projections for Marla Bay are similar but at lower abundance due to slower growth rate and slight differences in the matrix population model. Lake Tahoe. We found that the number of individuals of all stage classes could potentially double within 10 years (Figure 23. Figure 23 also shows the relative contribution of each size class to the population size over this 10 year period. Optimal management of this species should target those size classes which have the greatest reproductive value (i.e., > 10 mm) or the greatest contribution to relative population size and growth (i.e., the 1 – 5 mm size class). This population viability analysis indicates that the size class structure of Asian clams as observed in Lake Tahoe is indicative of an increasing population, and at the lake-­‐wide scale, this population is capable of doubling within a ten year period. This model is only based on demographic parameters (i.e., birth, death rates, fecundity, growth) of the population and does not consider environmental or habitat driven determinants of localized population growth rates. In other words, these environmental factors may drive population growth at different rates in different portions of the lake, altering rates of dispersal, fecundity and mortality in site specific locations. This modeling effort indicates that the reproductive potential for Asian clams in Lake Tahoe is positive and that population densities are likely to increase. 45
Asian clam dispersal: Sediment preference trials Background of habitat preference based on substrates. Many substrates can support the successful invasion of Asian clam, including cobble, gravel, mud, fine slit and sand with clams having the greatest success in sandy substrate (McMahon 1999.) In laboratory and lotic field experiments, Belanger et al., (1985) found that Asian clam had a preference for fine sand over course sand. In the lower San Joaquin River, California, Asian clam occupy a variety of substrate, none of which has been found to limit population densities (Brown et al., 2007). To determine if Asian clam from Lake Tahoe has a particle size preference for its bottom substrate, two trials were conducted. One was particle sizes of similar distributions from Nevada Beach, Emerald Bay and Crystal Bay (abundances of clams high, extremely low and absent at each site, respectively). The second trial included three classes of particle size, >75% silt, very fine gravel and medium sand, respectively. Methods Particle size characterization was conducted for each location. Approximately fifty (50) grams of substrate from each location was wet sieved through a series of decreasing size mesh from 3.962 mm to 53 µm. Each portion of the original sample was weighed (g) to determine the portion of the particles to the overall sample and particle size distribution was characterized using the Wentworth classification of particle size was be utilized (Gordon et al., 2007). We conducted two separate sediment preference, behaviorial trial experiments to determine if clams have a preference of substrate for establishment. The experiments were conducted over three days in a laboratory environmental chamber maintained at 18°C. The first trial was six treatments with particle sizes distributions of similar proportions from Nevada Beach, Emerald Bay and Crystal Bay (5 m depth for each location). These sites were chosen due to the level of C. fluminea at each site (Nevada Beach has very high abundances, Emerald Bay low abundances at the time this study was conducted as part of the larger project, and no clams are present in Crystal Bay). The second trial included five treatments of varying particle sizes: greater than 75% silt, very fine gravel and medium sand. Treatments were placed in circular containers with (Figure 24) an area of 285cm2 and equally divided into thirds (95cm2). Each was filled with treatment substrates to a depth of 4 cm and supplemented with aerated lake water at the beginning of each trial. Eleven clams per replicate per trial (15.16±0.56 mm for site experiment and 15.33±0.50 mm for particle size experiment) were mixed and randomly selected prior to each trial and placed in the center of each treatment. Each preference trial ran for 24 hours, where at the conclusion, the location of each C. fluminea was determined and recorded. Water was drained and refreshed prior to the start of each trial. Clams were collected from Marla Bay and Nevada Beach from a 5 m water depth. A two-­‐way ANOVA was used to assess the pattern of Asian clam dispersal to each substrate. 46
Figure 24. Substrate preference trials. A: Initiation of a trial without clams at the origin. A1: Clams placed at the origin of each treatment. A2: At the conclusion of 24 hours. B & C: Close view of all three substrates. Circles represent clams filtering in the substrate. Paths of exploration are clear in the Nevada Beach portion into Emerald Bay in B, and along the perimeter in C. Results and Discussion Particle size distribution differed between three sites (Figure 25) with the Nevada Beach site comprised mostly of medium sand (85%), with approximately 10% very coarse sand and 2% very fine gravel. The other two sites (Emerald Bay and Crystal Bay) were not comprised of any of the larger particle sizes (very coarse sand or very fine gravel) and contained greater proportions of fine and very fine sand than Marla Bay. Based on the results of this experiment, most sandy substrate in Lake Tahoe is suitable habitat for Asian clam establishment. While an ANOVA of the cumulative abundance of C. fluminea per treatment over the trial period was indicated there was a significant preference (p<0.0001) for Crystal Bay substrate over the other two treatments (Figure 26), there was no significant difference (p=0.19) for particle size (Figure 27, Table 6). Results from the habitat preference trials indicate that overall Asian clams had similar preferences for all substrate types considered (silt, sand, gravel). According to results published in Herold et al. (2007), there are approximately 3600 hectares of sandy substrate in the nearshore zone of Lake Tahoe. Clams have shown a slight but insignificant preference for larger particle sizes (very coarse sand, very fine gravel) based on results presented herein and based on field observations of high density populations in large sediment particle size locations (i.e., Marla Bay, Nevada Beach), but are capable of establishing in smaller sediment types such as those considered here (i.e., Emerald Bay, Crystal Bay) and from field observations of Asian clams in fine sand environments in Lake Tahoe (Lakeside, Zephyr Cove, Emerald Bay). 47
Figure 25. Particle size distribution for Nevada Beach, Emerald Bay and Crystal Bay. THIS PORTION IS INTENTIONALLY LEFT BLANK TO THE END OF THE PAGE. 48
Figure 26. Percentage of Asian clam per habitat type over 5 trials. Figure 27. Percentage of Asian clam per particle size over 5 trials. Table 6 : Two-­‐way ANOVA for Specific Particle Size Preference (silt, gravel or sand) Source DF F-­‐Value P-­‐Value Replicate 4 0.19 0.940 Substrate 2 0.95 0.393 Replicate*Substrate 8 0.34 0.945 49
C. What is the role of lake currents in the transport of larval/juvenile stages to noninfested locations? Background to transport model During the last few decades, the introduction of aquatic invasive species has become one of the major ecological and economic threats to lakes and waterways worldwide (Wilcove et al. 1998; Ricciardi and MacIsaac 2000; Pimentel et al. 2005). In the US alone, there are about 50,000 invasive species (terrestrial and aquatic) established that cause economic losses assessed at more than $120 billion per year (Pimentel et al. 2005). The introduction of invasive species may cause dramatic changes in an ecosystem through perturbations in the inter-­‐specific competition, predator-­‐prey interactions, food web structure, nutrient dynamics, hydrologic cycle, and sedimentation rates. Those changes typically lead to the displacement of native species from their natural habitats. The pressure posed by invasive species on native organisms is of such magnitude that their introduction has been ranked second only to habitat loss in the factors that threaten native bio-­‐diversity at the global scale (Vitousek 1990; Wilcove et al. 1998). The development of management guidelines for early detection and eradication appears as the primary tool to maintain the ecological integrity of un-­‐invaded habitats (Simberloff 2003; Vander Zanden and Olden 2008). These guidelines, in turn, need to be grounded on the sound understanding of the mechanisms by which invasive species spread and colonize new habitats, and the availability of mathematical models capable of forecasting the spatial dynamics of their populations. Forecasting the future spread of non-­‐indigenous species to new locations, however, remains difficult due to the complex interactions among non-­‐indigenous and indigenous species, humans, and local environmental conditions (Ricciardi and Rasmussen 1998; Rejmánek 2000; Moles et al. 2008). Most studies on the spread of invasive species in lenthic habitats have focused on the mechanisms of dispersion between water bodies, representing aquatic island ecosystems isolated from others by extended areas of non-­‐suitable terrestrial habitats (Figuerola and Green 2002). Dispersion in this case is largely mediated by human activities (Green and Figuerola 2005). For example, the pattern of recreational boating traffic among inland water bodies in Wisconsin is a good proxy for the spatial distribution patterns of the aquatic invasive bivalve Dreissena polymorpha (zebra mussel) (Buchan and Padilla 1999). Adult and juvenile mussels tend to attach primarily to macrophytes that entangle on boat trailers (Johnson et al. 2001). Once established in a given lake, the local dispersion of invasive species from colonized to new un-­‐colonized areas can also be driven by natural processes, such as water currents (Prezant and Chalermwat 1984). The role of water currents in the local dispersion of invasive species, however, remains largely un-­‐documented (Prezant and Chalermwat 1984, Tapia et al. 2004). This work focuses on the local dispersion of invasive species by wind-­‐driven currents in lakes. In particular, we address the problem of predicting the local dispersion patterns and spatial evolution of the invasive species Corbicula fluminea in Lake Tahoe, a large sub-­‐alpine lake on the crest of the Sierra Nevada mountain range (CA-­‐NV) at an altitude of 1898 m a.s.l. (Fig.1). The bivalve Corbicula fluminea, Asian clam, is among the most aggressive freshwater invaders worldwide (Morton 1979; McMahon 1999). In the United States, it was first detected in the late 1930s along the Columbia River in Washington State. Since then, its spread has been 50
both rapid and extensive. Nowadays it is found in water bodies of 39 states. The invasion success of Asian clam is based on its rapid population growth, early sexual maturity and short turnover time rather than on its tolerance to environmental fluctuations (McMahon 2002). The species is extremely sensitive to low oxygen conditions and requires sustained water temperatures of 15-­‐16°C or above for reproduction (McMahon 1999, Sousa et al. 2008, Wittmann et al. 2012). Asian clams generally form colonies or mats with densities that may exceed 6000 clams m-­‐2 (Aldridge and McMahon 1978, Wittmann et al. 2012), preferably in areas of coarse and sandy sediments (Karatayev et al. 2003). As a result of its high filtering capacity, C. fluminea filters out phytoplankton and other particles suspended in the water column which are also important food sources for other filter-­‐feeding organisms. It further uses its pedal foot to feed on organic matter in the sediment and competes for food resources with other benthic organisms (Hakenkamp et al. 2005). C. fluminea can also affect aquatic ecosystem processes in other ways. For instance, it excretes significant amounts of inorganic nutrients, particularly nitrogen that, in turn, can stimulate the growth of algae and macrophytes (Lauritsen and Mozley 1989, Sousa et al. 2008). Asian clam facilitates the introduction of parasites, diseases and other invasive species (Vaughn and Hakenkamp 2001, Sousa et al. 2008). In particular, C. fluminea has been shown to facilitate the invasion of zebra or quagga mussels by creating localized high calcium environments as dead shells leach this potentially limiting element (see Hessen et al. 2000, and references therein). Carried by water currents clams are able to enter intake pipes of power and water utilities where they attach themselves to the walls via byssal threads and grow, to ultimately obstruct the flow of water. The consequent economic losses caused by C. fluminea for the United States industries have been estimated at about a billion US dollars each year (Anon., 2005). In Lake Tahoe, C. fluminea was first observed in 2002 in very low numbers (Hackley et al. 2008), but its population has increased to a level where it is now having apparent environmental impacts. Its current known distribution (area ≈ 106 m²) is patchy along the southeast shore, with the largest population established in Marla Bay (Fig.1). This distribution is rapidly changing, though, due to its rapid growth rate and its ability to colonize the abundant sandy bottom existing along the shoreline in Lake Tahoe (Wittmann et al. 2008). The local dispersion of C. fluminea largely occurs during the larvae and juvenile stages of their life, when their low density (total dry weight of 0.1mg at ~200µm shell length, Aldridge & McMahon 1978) permits them to stay suspended in the surface mixed layer even under minimal turbulence (McMahon 1999). Larvae (pediveligers) and juveniles are not able to actively swim, but can travel long distances attached to boats or drifting with water currents and not only by diffusive growth. The contribution of wind-­‐driven currents in the local dispersion of C. fluminea in Lake Tahoe, from the existing beds in Marla Bay to neighbouring bays and beaches, however, is not known. Clams exposed near the sediment-­‐water interface can be lifted from the substratum and drift in the water column with currents through frictional drag (Prezant & Chalermwat 1984; Forrest et al. in preparation). Juveniles may further increase the drag by using their hydrophilic byssal thread as a drag line (Kraemer 1979, Prezant & Chalermwat 1984). Passive (natural) hydraulic transport by water currents is considered to be the main mechanism for the dispersal of C. fluminea (McMahon 1999). Studies on hydraulic transport have been carried out both in the laboratory and the field (Prezant & Chalermwat 1984, Williams & McMahon 1989), but have focused mainly on adult specimen. River currents are generally strong, unidirectional 51
and steady when considered on the time scale of days. Lakes currents, in contrast, being largely forced by winds, are characterized by lower magnitude as well as a higher temporal and spatial variability at hourly-­‐scales. The goal of this modelling is to characterize the pathways of transport of young life stages of Asian clams from existing beds to other near-­‐shore areas by wind-­‐driven currents in Lake Tahoe. In particular, the aim is to assess the risk of Asian clam to colonize different bays from the existing beds in Marla Bay, where the largest populations have been reported. A mechanistic, individual-­‐based model is first developed and later applied to conduct the assessment study. The model relies on the predictions of a three-­‐dimensional hydrodynamic model that has been applied previously with success to analyze surface currents in Lake Tahoe (Hoyer et al. 2012). Several key questions are addressed in this work, regarding the colonization strategy of the Asian clam. First, we establish the dominant mechanisms causing the pediveligers to be lifted from the sediments into the water column and the frequency at which juveniles are re-­‐suspended, initiating their travel in the pelagic. We identify the most likely pathways of migration of juveniles with lake currents from the beds with largest populations of clams (in Marla Bay) to other locations in the lake. Based on these pathways, we will evaluate the environmental conditions (temperature and light intensity) experienced by the particles during their trajectory. Finally, we propose a survival model, which will allow us to establish the probability of juveniles to colonize new near-­‐shore areas. This work is organized as follows. First we describe the conceptual model designed and used to represent the behavior of larvae (larvae model here after). Next, we outline the modeling tools (i.e. wave, hydrodynamics and particle tracking model) the larvae model is based on. Next, the results of the particle resuspension and particle transport by wind-­‐induced currents are presented. Finally, those results are analyzed and interpreted in terms of risk of colonization of new areas in Lake Tahoe. Methods A lagrangian individual-­‐based model has been developed to predict the dispersion of Asian clam larvae by wind-­‐driven lake currents, and assess the risk of colonization of new areas, from localized areas where C. fluminea is known to exist. The individual-­‐based model consists of three modules, run sequentially but independently of one another. The first module (release module) represents the growth of larvae in existing beds, and their incorporation into the water column through re-­‐suspension. The second module simulates the passive dispersion and settling of larvae during a period of time T that larvae remain viable in suspension. This module, referred to as transport module, tracks the position and the environmental conditions (water temperature and radiation levels) experienced by each individual. Finally, the third module represents the probability of larvae survival as they travel through the pelagic and settle to the sediment (survival module). Individuals are assumed to have colonized new near-­‐shore areas (i) if they settle at favorable habitats and (ii) if the environmental conditions endured in the pelagic do not affect their viability. Based on the three modules, this model is termed RTS model (for Release-­‐Transport-­‐Survival). The different modules are designed so that they can have different time resolutions Δt and, thus, different number of time steps n, where the subscript refers to the specific module, i.e. ΔtR, ΔtT, ΔtS and nR, nT, nS, respectively.. A Cartesian Grid is used to discretize the physical domain (i.e. the lake). Hence, the horizontal larvae 52
locations in the computational grid will be indicated by the E-­‐W and N-­‐S indexes (i, j). The model is developed under the assumption that water temperatures during the study period are favourable for clam health and larvae release. The range of favourable temperature is defined by the critical value necessary for reproduction θmin and the lethal value θmax. The specific values for Asian clam are given in Table 6. Release module The larvae population existing at the lake bottom at any given location (i, j) of the computational domain, on time step nR will be referred to as Lij(nR). The population size Lij will change in time as a result of (1) adult reproduction; (2) larvae death or other processes reducing the number of juveniles; and (3) larvae resuspension by physical processes in the water column. The number of larvae existing at time nR+1, Lij(nR+1) is calculated as a function of the population at the previous time Lij(nR) as follows Equation 6 Lij (n R + 1) =Lij (n R ) + µ g Aij Δt R − µ d Lij (n R )Δt R − δ R (1) (2) (3) The terms (1)-­‐(3) represent the three processes contributing to the growth or decay of the number of larvae. The production of new larvae (term 1) is modeled as a zero-­‐order process, with a production rate μg proportional to the number of adults A existing at site (i, j), Aij. For simplicity, the population Aij will be constant in time, which is partly justified in that Asian clam larvae reach maturity 3-­‐9 month after their release (Sousa et al. 2008). Hence, newly released juveniles will not participate in reproduction during the 2-­‐month study period. The decay processes (term 2 in Equation 6), in turn, are assumed to follow a first-­‐order model. The decay rate, μd, represents the fraction of the larvae population that becomes unavailable for dispersion per unit of time. Finally, the third term on the right hand side of Equation 6 represents the effects of re-­‐suspension processes on the larvae population. Here we assume that larvae behave like passive and negatively-­‐buoyant inorganic (sediment) particles. Hence, larvae re-­‐suspension can be parameterized in terms of the ratio of the bottom shear (or friction) velocity u* to the particle settling velocity ws, u*/ws (Bagnold 1966), widely used for sediment resuspension. The shear velocity at the bottom u* is largely determined by the hydrodynamic conditions (wave-­‐induced orbital motions and current-­‐induced motions) at the sediment-­‐water interface, and will vary in space and time. Current-­‐induced shear stresses are calculated by a 3D hydrodynamic model (e.g. Luettich et al. 1990); wave-­‐induced shear velocity, in turn, is calculated using the phase-­‐averaged spectral wave model of Smith et al. (2001) (see Wave and hydrodynamic variables). All individuals existing at time nR on cell (i, j) will be re-­‐
suspended and incorporated in the water column if the ratio u*/ws > 1 (Bagnold, 1966). In that case, the larvae population at that cell is ‘reset’ to zero i.e. Lij (nR+1) = 0. Otherwise, the population will continue growing. Consequently, the last term δR accounting for the effect of re-­‐
suspension processes on larval growth takes the following non-­‐linear form ⎧
u * (n R )
⎪ Lij (n R ) + µ g Aij Δt R − µ d Lij (n R )Δt R
if
≥1
Equation 7 δ R = ⎨
ws
⎪⎩
0
otherwise
53
Note that this resuspension term links the release module to the transport module. All juveniles existing on cell (i, j) and re-­‐suspended at time nR, become available in the water column for transport by lake currents (simulated in the transport module) at time nR+1. The (re)suspended larvae are presumed uniformly distributed either over the water column of depth H (i, j), or over a fraction f of H. To parameterize the height above the sediments up to which the particles will re-­‐suspend, we use the non-­‐dimensional suspension number Z proposed by Rouse (1937) (also known as Rouse number), ws
κ ⋅u *
Z=
Equation 8 where κ is the von Kármán constant (= 0.4). For 1.2 < Z < 2.5, the sediments are suspended up to mid of the water depth (f = 0.5); and for Z < 1.2, the sediments are suspended up to the water surface (f = 1). Transport module The transport module simulates the horizontal and vertical displacement of larvae and the environmental conditions experienced during this displacement The position of the larvae, re-­‐
suspended from the existing clam beds, is simulated using the three-­‐dimensional 3D time varying particle tracking model proposed by Rueda et al. (2008). In this model, the trajectories of negatively buoyant particles (i.e. larvae) are calculated as the summation over time of successive infinitesimal particle displacements dxi (i = 1, 3), of the form, each one consisting of a deterministic part and a stochastic part, i.e. 3
dxi = ai (x, t ) ⋅ dt T + ∑ bij (x, t ) ⋅ dW j (t ) Equation 9 j =1
The first term on the right hand side represents a deterministic part of the displacemente, and the second term a stochastic part; Wj is a Wiener or Brownian motion process and the coefficients ai and bij need to satisfy the following conditions (Dunsbergen and Stelling 1993) 3
∂Dij
j =1
∂x j
ai = u i + ∑
1
3
2∑
k =1
bik b jk = Dij Equation 10 Equation 11 Here, x and u are the particle position and velocity, respectively, and Dij is the isotropic diffusion tensor in the flow field. In general, the deterministic term accounts for the effects of large-­‐scale coherent motions (currents), while the stochastic term represents the effects of turbulent unresolved motions. Note that the deterministic part also includes the influence of the gradient of turbulent diffusivity, needed for consistency with the advection-­‐diffusion equation (see Dimou and Adams 1993, Kitanidis 1994). The velocity at the present position of a given particle is interpolated, as proposed by Pollock (1988), from the velocity predicted at the grid points by the 3D hydrodynamic model. The settling velocity is superimposed on the vertical component of the water velocity field. The vertical eddy diffusivity Kz at the cell faces (where it is defined in 54
the hydrodynamic model, Rueda 2001) is interpolated linearly to obtain the values at the particle position. The discretization of Equation 9 that represents the transition from the state (or particle position) at nTΔt to the state at (nT+1) Δt is given as 3
xi (nT + 1) = xi (nT ) + ai (x(nT ), nT ΔtT ) ⋅ ΔtT + ∑ bij (x(nT ), nT ΔtT ) ⋅ ΔW j (nT ΔtT ) Equation 12
j =1
At the start of the simulation t0, a number of particles N0 are uniformly distributed over a given fraction f of the water column at the horizontal cell (i0, j0). The release of an elevated number of particles N0 is required to guarantee statistically significant results in each experiment. Each of those particles tracked in any given simulation is represented by the index l (l = 1, N0). We will identify each simulation by the location and time of the release, and the fraction of the water column seeded, i.e. (i0, j0 t0, f). The position of particle l at time t will, then, be referred to as R (l, t | i0, j0, t0, f). The particles may remain in suspension, or they may settle. If they settle, they may remain attached to the bottom or they may re-­‐suspend, with a probability of expressed by a parameter ηs, referred to as attachment efficiency. For ηs = 1, all larvae that settle will remain on the sediments there after. For ηs < 1, a fraction ηs of the particles settled can be readily re-­‐suspended if the hydrodynamic conditions for resuspension exist (i.e. if u*>ws). Once attached to the bottom, the particle position will remain constant during the simulation time and will be given by R = (x l, y l, z l), with zl = -­‐H(x l, y l), i.e. the water column depth H at the horizontal position (x l, y l). Resuspended larvae that re-­‐settle within the original clam beds, will remain attached to the sediments, i.e. ηs = 1. Although these larvae add to the local larvae population they play no active part in resuspension and, therefore, are not taken into account for resuspension nor transport at the following time step. The model also tracks the temperature and radiation levels experienced by each individual (i.e. their life histories) during the simulations period T, θ (l, t | i0, j0, t0, f) and I (l, t | i0, j0, t0, f) respectively. Of particular interest for the survival of clam larvae is the solar radiation in the ultraviolet (UV) wavelengths, which is well known to negatively affect aquatic organisms (UV-­‐B, 280-­‐315nm) (Chalker-­‐Scott et al. 1994; Sinha and Häder 2002; Häder 2003). This fraction of UV-­‐radiation (UVR) is estimated as ~1% of the global radiation (Grant et al. 1997). The UVR reaching a given individual l at time t is calculated from its vertical position z(l, t) and the incident UVR reaching the free surface I0(t) (Wm-­‐2), as follows, I (l , t ) = I 0 (t ) ⋅ exp[− kUV (t ) ⋅ z (l , t )] Equation 13 where kUV (t) is the UVR specific attenuation coefficient (m-­‐1) set to 0.14 in accordance with observation for ultra-­‐oligotrophic Lake Schmoll (Argentina) (Morris et al. 1995) and other clear lakes (Smith and Baker 1981, reported in Morris et al. 1995). The time-­‐dependent UV-­‐intensity reaching a given particle is then used to calculate the UV-­‐dose over a given period of time. The light dose experienced by the individual l, as it travels in the pelagic, Id (l) (Jm-­‐2), is calculated as the amount of UV-­‐energy received, as follows t
Id (l , t ) = ∑ I (l , t ) ⋅ ΔtT t0
55
Equation 14 Here ΔtT is the time step of the transport module. Note that the dose is an accumulative variable, and represents the energy above harmful levels that a given individual has received, from the time t0 when it was resuspended to the time it settles at any given site, ts. After settling, the individuals are assumed to burrow in the sediments, avoiding the harmful radiation. At the end of each simulation, the transport module outputs the position of each particle R, the temperature experienced (maximum, minimum, and average values) and the UVR (dose) received by each individual during time T. Survival module The survival module uses the final particle position and particle life histories simulated in the transport module to evaluate the probability of colonization. The probability P that juveniles, released from site (i0, j0) at time t0, will colonize new near-­‐shore areas outside existing beds after transport period is evaluated for each cell (i, j) in the computational domain. P depends on (1) the probability of the larvae reaching a given horizontal cell (i, j) PT, and (2) the viability of those larvae PV, i.e. P(i, j, t0 + T | i0 , j0 , t0 , f ) = PT (i, j, t0 + T | i0 , j0 , t0 , f ) × PV (i, j,0 t0 + T | i0 , j0 , t0 , f ) Equation 15 The probability PT, (first term on the right hand side) depends on the interaction between the physical processes of transport by currents and turbulence, and particle settling. The second term, PV, in turn, represent the probability that larvae reaching a horizontal location (i, j) are viable to grow and, thus, colonize the lake sediments. The number of particles that were released at cell (i0, j0) and time t0, and are deposited at a given location (i, j) on the lake bottom at time t = t0+T is referred to as N (i, j, t0+T | i0, j0, t0, f) and was calculated as follows, N0
N (i, j, t0 + T | i0 , j0 , t0 , f ) = ∑ δ [R(l , t0 + T | i0 , j0 , t0 , f ), S(i, j, km (i, j ))] Equation 16 l =1
Here km(i, j) is the index used in the model to point to the cell closest to the bottom in any given water column (i, j), and S(i, j, km) is the spatial domain of that bottom cell. The function δ is defined as follows, = 1 if R (l , t 0 + T | i0 , j0 , t 0 , f ) ∈ S(i, j , k m )
δ (R, S) = ⎧⎨
=
otherwise
⎩ 0
Equation 17 The probability of a larva reaching the lake substratum after a period of time T, is then calculated as the ratio of the number of particles deposited at that cell by the total number of particles initially released PT (i, j, t 0 + T | i0 , j0 , t 0 , f ) =
N (i, j, t 0 + T | i0 , j0 , t 0 , f )
N 0 (i0 , j0 , t 0 , f )
Equation 18 The viability of the larvae reaching any given site is a function of (1) the local habitat conditions and (2) the ambient conditions along the migration paths. Hence, the probability of a 56
given individual, that has reached site (i, j) within time T after release, to grow and establish is represented as follows, PV (i, j , t 0 + T | i0 , j 0 , t 0 , f ) = PH (i, j ) × PS (i, j , t 0 + T | i0 , j 0 , t 0 , f ) Equation 19 The first term refers to the probability that the juvenile encounters suitable conditions at the local habitat and is measured in terms of the type of substratum and the water column depth at the point settled. The favourable depth for particle survival is defined by the upper and lower limit for colonization. Hence, ⎧= 1 if H min < H (i, j ) ≤ H max
PH (i, j ) = ⎨
otherwise
⎩ = 0
Equation 20 The limits of water column depth Hmax and Hmin are species and site specific and can be set to any values. The second term on the right hand site of Equation 19 represents the probability of survival and colonization due to the life histories. The survival of a larva evaluated based on (1) the probability PS(l, t0) of death for that individual based on the temperature and light history, (2) a generated random number a from a uniform distribution in the range [0 1]. The larva will survive for a ≥ PS; but will be marked dead for a < PS. If LD50 refers to the median lethal dose of light for the larvae, the probability of survival p due to the effect of light, i.e. UV radiation, is evaluated as (Shen et al. 2008) p(l , t0 ) =
1
m
⎛ LD50 ⎞
1 + ⎜
⎟
⎝ Id (l , t0 ) ⎠
Equation 21 where m determines the slope of the probability function. Asian clam dispersion in Lake Tahoe The RTS model was applied to Lake Tahoe to simulate the dispersion of Asian clam juveniles during their reproductive period. Water temperatures of Lake Tahoe exceed the threshold value for clam reproduction (i.e. θ > 15oC), on average, between the beginning of July and end of September. However, Denton et al. (2012) observed a time lag of a minimum of 4 weeks between the time when water temperatures start to be suitable for Asian clam reproduction and the time when clams actively reproduce. Therefore, the study period starts at the beginning of August (day 214), after a month of suitable water temperatures, and lasts for two months, until day 274. Ambient temperatures above 30°C, being lethal for Asian calms and their larvae, are seldom attained in temperate or sub-­‐alpine lakes and were not recorded at Lake Tahoe during the study period. The distribution of adults is shown in Figure 30A, all other locations in the lake were assumed non-­‐colonized. At the start of the 2 month period, the larvae population was assumed 57
negligible (i.e. L(0) = 0). The R-­‐module was run with a time step ΔtR = 6 h. At the same time interval ( Δt0 =6h), particle (i.e. Asian clam larvae) were initiated at each horizontal grid cell in Marla Bay in the T-­‐module. The number N0 of particles releases at the start of each simulation was set to 104. For particles in excess of 104, results were found to be independent of the number of particle released. This number of particles is smaller than that used by Biferale et al. (2005) or Marchioli et al. (2007) for particle statistics in turbulent flow (simulated by direct numerical simulations). The simulation period of this module, T, is 2 days, equal to the maximum length of time that larvae have been observed to persist in the pelagic (Kraemer & Galloway 1986). The input of predictions of current speed, current directions, and vertical turbulence in the 3D domain were provided at hourly intervals. The observed solar radiation was provided at 10 min intervals. Hydrodynamic and meteorological data were interpolated in time to the time steps of the T-­‐module (ΔtT = 10 s), satisfying the convergence criterion by Ross and Sharples (2004). The solution of the T-­‐module was output on at the end of each 2-­‐day simulation period (i.e. every 6h), and it was with this time interval that the survival module was run (ΔtS = 6h). The probability of survival and colonization was evaluated based on the parameters found in the literature. Asian clams are found preferentially on sandy beds, which is the most common type of near-­‐shore substrate along the shore line in Lake Tahoe (Wittmann et al. 2008). Hence, the lake substratum does not pose any limitation on the potential of colonization. The preferred depths of establishment are set here based on the observations of Wittmann et al. (2008), who found Asian clams between depths of 2 m (Hmin) and 39 m (Hmax). The probability of survival due to water temperature is equal to 1 as temperatures at Lake Tahoe are always within the acceptable range for Asian clam (3°C < θ < 30°C, McMahon 1999). For this case, the probability of survival due to ambient conditions Ps is equal to the probability of survival due to the effect of UV radiation only. No quantitative information exists in the literature on the sensitivity of Asian clam larvae to UVR. Hence, the values used here were taken from a studied carried out on a similar species, Dreissena polymorpha (Aquatic Sciences 1995). Aquatic Sciences (1995) studied the sensitivity of D.polymorpha to UVR in a laboratory flow through system and found that 50% of the population died after an exposure of ~45min to a constant UVR (365nm) of 2.85Wm-­‐2 (LD50 = 7000 Jm-­‐2). This light intensity is of the same order of magnitude as the incident UV-­‐B intensity at Lake Tahoe, calculated from the meteorological observations. On release Asian clam larvae are larger than those of zebra mussel and have a better developed (i.e. thicker) shell which makes them more resistant to harmful UV radiation (McMahon, pers. comm.). Therefore, as a first approximation for clam larvae, we will use LD50 = 5x105 Jm-­‐2 for the 2 day simulation period. The particular values and ranges of values for the kinetic parameters used in the model are given in Table 1, and are taken from the literature. Note that some of the parameter values are specific for Corbicula, but others are taken from studies conducted on other bivalves. For example, the decay rates of Asian clam larvae used in the R-­‐module is of the same order of magnitude as the decay rate observed for blue mussel (Mytilus edulis) larvae in the pelagic phase (Jørgensen 1981). This work, hence, should be considered as a first estimate to characterize the risk of Asian clam dispersal, and needs to be updated, once proper parameters for C. fluminea are found. Note also that two settling velocities are included in Table 1. The average settling velocity ws for non-­‐living juveniles of Asian clam has been determined empirically in the laboratory and is ~10-­‐3 ms-­‐1 (~86 md-­‐1, Chandra, pers. comm.). This value, however, will be taken here as an upper limit of settling 58
velocity, given that living organisms may exhibit settling velocities which are below their non-­‐
living counterparts with equivalent diameters (see, for example, Reynolds 1984). Further, at a settling rate of 86 md-­‐1, clam larvae seem unable to stay in the (seasonal) SML during the day when vertical eddy diffusivities below a depth of 2-­‐3m do not exceed O(10-­‐5)m2s-­‐1. A sedimentation rate of 8.6 md-­‐1 (10-­‐4 ms-­‐1) would guarantee that larvae stay suspended in the SML (of 15-­‐20m depth) for at least 2 days even under minimal turbulence. Consequently, we assume the larvae settling velocity to be between O(10-­‐4)ms-­‐1 and O(10-­‐3)ms-­‐1. The R-­‐ and the S-­‐
module were written in MATLAB. The source code of the particle tracking model is written in FORTRAN95. Wave and hydrodynamic variables The wave-­‐induced bottom shear stresses, used in the release module, are simulated using the phase-­‐averaged spectral wave model STWAVE, a two-­‐dimensional (2D) finite difference model developed by the U.S. Army Engineer Research and Development Center (ERDC, Smith et al. 2001). The model is based on the wave action balance equation (Smith 2007a) and has been previously used to model near-­‐shore waves in lake environments (Lake Eire, Chader et al. 2006; Lake Pontchartrain, Smith 2007b), with successful results in all cases. Wave generation is attained either under fetch-­‐limited or fully developed conditions. Variations in bathymetry, water level and currents are the main drivers of wave formations and cause changes in wave parameters on the scale of tens of meters. The steady state formulation assumes that wind conditions, that force the free surface, remain constant for a longer period of time than the time it takes for waves to generate. Wind records collected at ten meteorological stations around the lake (Figure 28) were filtered and provided at 6h intervals. This time step exceeds the maximal time scale for wave formation in observed oceans (~3h, Sanchez-­‐Badorrey, pers. comm.). Therefore, it allows for waves to generate and guarantees computational cost efficiency at the same time. The model takes into account spatially variable winds, encountered in large systems surrounded by complex topography, like Lake Tahoe. The interpolation method proposed by Barnes (1964) was applied to construct the spatially variable wind fields used to force the model. The same interpolation method was used to create the 10min wind field necessary to force the hydrodynamic simulations, used in the R-­‐module (currents-­‐induced shear) and the T-­‐
module. The hydrodynamic simulations of lake currents and mixing variables were carried out using the using the 3D hydrodynamic model of Smith (2006), based on the numerical solution of the 3D form of the shallow water equation. The model has been modified and extensively validated both against analytical solutions (Rueda and Schladow 2002) and field data sets (Rueda et al 2003; Rueda and Schladow 2003; Rueda and Cowen 2005), which provides support to the validity of its predictions. The model was specifically validated for this study against available observations of basin-­‐wide, near-­‐surface circulation, surface and bottom current velocities in shallow regions and vertical eddy diffusivity by Hoyer et al. (2012). The model grid was constructed with a spatial resolution of 100 m in both EW and NS directions. The vertical resolution was made to vary with depth, from ∆z = 0.5 m at the surface to ∆z = 10 m near the bottom (i.e. at a depth of 500 m). The bathymetry was taken from Gardner et al. (1998) and 59
corrected (Steissberg, pers. comm.) in the southern near-­‐shore region. For stability purposes, the time step of the hydrodynamic model was set to 50s. Results and discussion Larval development and re-­‐suspension in existing beds Shallow regions, like those colonized by Asian clams in Lake Tahoe (Figure 30), are frequently disturbed by wave-­‐driven orbital velocities acting on the lake bottom, and by lake currents in the bottom boundary layer (Luettich et al. 1990, Hamilton and Mitchell 1996). The relative contribution of currents and wind-­‐waves to bottom shear between 2 and 40 m in Marla Bay vary depending on the time and location considered (Figures 31 and 32). The contribution of wave-­‐driven shear tends to be larger in the shallowest depths, and particularly during the evening coinciding with the strongest winds. Current-­‐induced shear, in turn, tends to be larger during periods of calm and towards the southern shore of the Bay. In general, the contributions of currents and waves to total bottom shear in Lake Tahoe are similar in magnitude (Figure 31). Owens et al. (2011) found that sediment resuspension in a canyon-­‐shaped reservoir was attributed to current-­‐induced shear stress during events of elevated river inflow. Observations in two shallow lakes, Lake Balaton (Luettich et al. 1990) and Lake Okeechobee (Jin and Sun 2007), showed that wave-­‐induced shear stress was the dominant driver of sediment resuspension. However, Luettich et al. (1990) state that locally elevated mean currents may contribute significantly to sediment resuspension. Under the combined motions of waves and currents, re-­‐suspension occurs almost continuously between 2 and 40 m, with a few periods in which the lake bottom remains unperturbed (Figure 31). The probability or frequency of re-­‐suspension was estimated at each site as the fraction of time within the study period when the condition u*/ws > 1, for the reference settling velocity, holds (Figure 32). This frequency varied depending on the horizontal location within the bay, but, in general, the regions with the largest frequency of re-­‐suspension are in the South of the Bay, coinciding with areas with the maximal clam density and with the largest contributions of currents to bottom shear (Figure 30). Hence, the largest re-­‐suspension fluxes incorporating larvae from the lake bottom into the water column are expected to occur in the Southern end of the basin. The simulated number of larvae existing on the lake bed tends to exhibit variations at a range of scales from diurnal to synoptic, largely driven by changes in wind forcing, and, hence, in currents and waves in the coastal areas. Maximum larval populations, for example, tend to occur in the afternoon, prior to the wind events, and minima at night as a result of the diurnal periodicity of wind forcing. The larval population also undergoes changes as a result of the synoptic scale changes in wind forcing. The magnitude and frequency of those changes, though, are very sensitive to the settling rate ws considered (Figure 33). For the reference value (ws = 10-­‐
3
ms-­‐1) the population size is of O(106) ind.m-­‐2 (Figure 33A), with large and episodic fluctuations, resulting from re-­‐suspension pulses of variable magnitude associated to strong wind events. The magnitude of these pulses depends on the area affected by re-­‐suspension during those events, and the frequency of re-­‐suspension during the time prior to the event. The largest re-­‐
suspension event, for example, occurred on day 231, after a period of time (from day 225-­‐231) 60
with prevailing calm conditions, hence, with little perturbations in the lake bottom. During the 5-­‐day period prior to the event the number of larvae grew up to 4x106 ind.m-­‐2. During the event, a strong pulse of ca. 100 ind.m-­‐2s-­‐1 was re-­‐suspended in the water column, reducing the larvae population in order of magnitude to O( 105) ind.m-­‐2. For a settling velocity of 10-­‐4ms-­‐1, both the population size and the magnitude of the re-­‐suspension pulses are more stable but lower than their values simulated with the reference settling rate (Figure 33B). The size of the larvae population in this case varies between 0 ind.m-­‐2 and 5x104 ind.m-­‐2, and the fluxes of larvae are of O(10) ind.m-­‐2s-­‐1. The simulations are, in general, weakly sensitive to changes in growth or mortality rate in the model. For example, a ±10% change in the growth rate results in a ±10% change in the larvae population, consistent with the zero-­‐order nature of the growth model (Equation 6). The effect of ±10% change in the mortality rate is weaker (±6%), given the first order kinetics used to simulate the decay of larvae in the bed. Circulation in Marla Bay Currents in Marla Bay are largely driven by local winds. The depth of the seasonal mixed layer SML during the study period varied from 10m, on day 214, to 20m, on day 275, and the stability of the water column was large compared to the strength of the wind. The Wedderburn W and Lake numbers LN (Stevens and Imberger 1996), used to parameterize the balance between thermal stability and wind forcing, and calculated from observed temperature profiles and wind records, were well above one (i.e. W >> 1, LN >> 1) at all times. Assuming a two-­‐layer stratification with an upper mixed layer of thickness H, the displacement of the isotherms Δh driven by wind forcing can be estimated in terms of W, as Δh = 0.5H/W [Shintani et al., 2010]. For Lake Tahoe during the study period isotherm displacements were at most 3 m, and hence, the metalimnion did not reach the region above the 6 m isobath in Marla Bay, where the largest population of clams exist. Horizontal density differences perpendicular to the lake shore in Marla Bay driven by differential heating and cooling processes were also, in general, low. Hence, baroclinicly driven exchange driven either by basin scale internal waves or heating differences between the bay and the pelagic was negligible compared with the direct effect of local winds acting on the free surface. Winds during the study period are predominantly aligned with NW-­‐SE axis, with speeds <10ms-­‐1 (Figure 34), and subject to diurnal changes (Figure 35). The strongest winds are from the SW, in the evening. Winds, in turn, are typically moderate (< 6ms-­‐1) and from the SE at night and morning, and weak (< 2ms-­‐1) and from the NW in the afternoon. This diurnal pattern becomes perturbed on synoptic scale during events of strong wind forcing, with speeds of ca. 10ms-­‐1, as occurred, for example, on days 221-­‐223 and days 243-­‐245. In general, the along-­‐shore components of the currents are in the same direction at all depths, while the cross-­‐shore components appear at times to exhibit different directionality at the surface or near the bottom. Being linked to wind forcing, currents magnitude and direction exhibit changes both at diurnal and synoptic scales. Near the surface (Figure 36), for example, water tends to flow into the bay in the evening, in response to the typically westerly (on-­‐shore) winds, but, out of the bay under calm conditions, at night and early in the morning, as a result of the predominantly easterly winds. Near the bottom currents (Figure 37), in turn, tend to flow 61
in the opposite direction. These temporal changes are particularly strong and clear in response to the stronger events. The strongest currents (typically northwards and out of the bay) are simulated at the southwestern edge of the bay, over the shelf region between Timber Cove and Marla Bay, where the shallowest depths and the largest population of larvae are located. Residual currents (Figure 38) are into the bay in the deeper layers, and out of the bay in the shallowest layers. The highest residual flows out of the bay (> 6 x10-­‐3ms-­‐1) are observed at the southern edge. The largest inflow velocities into the Bay, occur below the mixed layer (>20m). Highest magnitudes are of 0.06ms-­‐1 between 30m and 40m depth near the deepest part of the bay. This circulation pattern suggests that larvae that are released and resuspended in the southern bay, where currents flow out of the bay at highest magnitudes, are most likely to reach new areas for potential colonization. Larval transport and dispersion out of Marla bay Larvae transport and dispersion is sensitive to the vertical particle settling velocity ws. Four indicators are used to compare model results: (i) the maximal particle distance Dmax from the original point of release (x0, y0) at the end of the simulation (t = 2d), (ii) the extension of the particle cloud along EW and NS axis after t = 2d, (iii) the dispersal rate after t = 6h and t = 2d, and (iv) the fraction of settled particles after t=6h and t=2d. The dispersal rate is parameterized as the number of particles that have left the area of the original particle cloud A0 after a given time t and expressed as fraction of the total particles released, i.e µD=1-­‐(N(A0,t)/N(A0,t0), following Tang et al. (2006). In general, maximal distance and cloud extension increase, and dispersal rate and settled particles decrease with decreasing settling velocity. The distance Dmax for ws=10-­‐4ms-­‐1 is 2-­‐8 times higher than those calculated with ws=10-­‐3ms-­‐1. The same trend is observed for the spatial expansion of the final particle cloud. At high ws, the NS extension exceeds the EW extension, in agreement with the fact that currents in the bay are stronger along this axis. At low ws, the relative extension along the two axes is related to Dmax: the extension in EW direction exceeds the one in NS direction when maximum distance travel is high (i.e. Dmax>6km). We conclude that inside the bay the NS velocity component dominates the particles transport and dispersion, while outside the bay the EW component of the water currents is the dominant one. Differences in Dmax and the cloud expansion are related to differences in the magnitude of the rate of horizontal dispersal µD. µD increases with increasing simulation time t and decreasing settling velocity ws. The longer the time t expired after the time of release t0 and the lower ws, the higher the number of particles transported away from the point of release. The settling rate determines the vertical particle position within the water column and the time the particle remains in suspension before it settles to the sediments. Therefore, at low ws, particles are exposed to relatively stronger currents during a longer period of time, increasing their chance to reach new areas for colonization. The results of the transport and dispersion model of lavae are highly sensitivity to the values of (1) horizontal eddy diffusivity and (2) initial spread of the particle cloud. For comparison, a series of simulations was repeated using three different order of magnitude of Kxy (10-­‐2m2s-­‐1, 10-­‐1m2s-­‐1 and 1m2s-­‐1). Results showed that the higher the horizontal eddy diffusivity Kxy, the stronger the larvae dispersion in terms of distance to the point of release and 62
horizontal expansion of the particle cloud. The initial size of the particle cloud also increases the rate of dispersion, in agreement with the classical results of Okubo (1971). The effect of cloud size is largely dependent on the site of the release, being weaker in the near-­‐shore areas as a result of the lower velocities and weaker shear. In the outer edge of the bay, in turn, and as a result of the larger shear values, dispersion rates tend to increase. Hence, a large initial cloud (which corresponds to a large extension of lake bed colonized by adults affected by re-­‐
suspension) will increases the variety of migration pathways and their chances of being transported out of Marla Bay to potential colonization sites. Pathways of migration and potential colonization sites Juvenile stage of Asian clam larvae reach potential colonization sites along the eastern coast of the lake, south and north of Marla Bay. Larvae are transported from the existing beds in Marla Bay with the water currents mainly towards the north in shore line section 7 (SLS 7, Figure 28), and only occasionally towards the south of the bay (SLS 8). The predominant migration path follows the shoreline towards the north along the northern cape of Marla Bay. A fraction of the individuals following this path will move across isobaths and enter the pelagic zone. Others, in turn, settle in SLS 7. Those entering the pelagic will not encounter suitable habitats within 2 days after becoming resuspended in the water column, when the larve are viable. Those settling, however, encounter potential colonization sites, mainly in the near-­‐shore area from Zephyr Cove (Figure 30A) up to the height of Cave Rock (Figure 28). The shallow depth (<39m) and the sandy substrate of this area provide an ideal habitat for Asian clam larvae. Migration towards the south of the existing clam beds is limited. Larvae that take this migration path only reach the northern part of Nevada Beach (Figure 30A). Other individuals are transported away from the littoral zone into the pelagic, Survival and probability of colonization The probability of colonization of near-­‐shore areas away from the existing Asian calm beds in Marla Bay is close to zero (Figure 39). The majority of the viable particles remained in the near vicinity of the already-­‐colonized beds of Marla Bay. Only a small fraction of larvae that settle in Nevada beach are viable. However, this region is also colonized already. Uncolonized near-­‐
shore areas north of Marla Bay from Zephyr Cove to Cave Rock have a risk of being colonized by Asian clam. However, the probability that viable larvae reach this region is less than 1% and decrease with distance from Marla Bay. Larvae released and resuspended in Marla Bay are most likely to either resettle near the point of resuspension or disperse locally within the bay. In this sense, newly released larvae add to the existing calm population rather than expanding to establish new populations. The average percentage of particles reaching the water column from the sediments during a resuspension event, and settling within the next 2 days varied between 60% and 95%. However, only 20% – 40% of the settled particles reached suitable habitat of a water column depth between 2 and 39m. The impact of UV radiation on the viability of the larvae was low. Over the 2 day period migrating larvae received light doses between 5 and 4x105 Jm-­‐2. Less than 1% of the larvae died because of a lethal radiation dose. In general, the light doses received did not affect the larvae survival. However, the probability of survival (and lethality) due to UVR 63
greatly depends on the critical value LD50. The value set for this study is justified in the sense that Asian clam larvae are more resident to UV radiation due to their well developed shell, compared to other bivalve such as the zebra mussel. However, given that little information exists in the literature on Asian clam, this value is somewhat arbitrary. Therefore, if a higher sensitivity of clam larvae to UVR is assumed the mortality of juveniles under suspension, and, hence, the probability of reaching new areas in a viable form would be reduced. This probability decreases with increasing distance migrated by the larvae, i.e. with increasing time the larvae spend in resuspension. Consequently, a higher sensitivity to UVR would confine the risk of colonization more closely to Marla Bay. The risk of colonization may be further reduced by lower larvae settling velocity ws. Here a maximal velocity of ws=10-­‐3 ms-­‐1 was assumed. At velocity of 10-­‐4ms-­‐1, for example, effect of turbulence and mixing would sustain the larvae in the water column for a longer period of time. This increase of resuspension period would also increase the effect of harmful UVR and, thus, decrease the number of viable larvae to colonize new near-­‐shore areas. Summary and conclusions • A three-­‐dimensional model of wind-­‐driven dispersion patterns of juveniles from existing beds areas has been developed. The model simulates independently, the processes of re-­‐suspension by currents and waves, transport by currents and survival of larvae on account of UV radiation and finding suitable habitat within 48 hrs. • For this report it has been assumed that the only source of juveniles is located in Marla Bay (Figure 30A) where the highest population of Asian clams have been located. A critical unknown is the juvenile settling rate, and settling rates in the range of 10-­‐3 – 10-­‐4 ms-­‐1 were examined. • The model results suggest that the dispersal of Asian clam larvae by wind-­‐induced currents occurs mainly on small spatial scales. Asian clam juveniles can potentially reach colonization sites along the eastern coast of the lake, both north and south of Marla Bay along two preferred pathways. They can also travel west across the lake along a third preferred pathway. However, the distance they can be transported is generally small before they resettle on the sediment, unless a very low settling rate is assumed. As a consequence the risk of new Asian clam infestation outside the existing beds in Marla Bay on account of transport of Marla Bay juveniles is close to zero, but still finite. • UV exposure has only a minor role on survival of juveniles, affecting only about 1% of the juveniles. Table 6. Model parameter specification. Symbol Parameter Value/Range Units Reference/comments tl 2 Reproduction period (Denton et al. 2012) Length of study period month 64
McMahon (1999) θmin Minimal temperature for reproduction 15 °C θmax Lethal temperature 30 °C McMahon (1999) Δt R Time step of release module 6 h -­‐ ΔtT Time step of transport module 10 s Ross & Sharples (2004) Δt0 Time between consecutive particle tracking simulations 6 h T Length of particle tracking simulation 2 d Kraemer & Galloway (1986) ΔtS Time step of survival module 2 d µg Larvae growth rate 10 d-­‐1 Denton et al. (2012) µd Larvae decay rate 0.1 d-­‐1 Jørgensen (1981) ws Larvae settling velocity 10-­‐4-­‐10-­‐3 ms-­‐1 Scaling arguements/ Chandra (pers. comm.) ρw Water density 103 kg m-­‐3 -­‐ N0 Number of released particles 104 ind. -­‐ ηs Attachment efficiency 0-­‐1 -­‐ -­‐ kUV Attenuation coefficient of UV radiation 0.14 m-­‐1 Morris et al. (1995) LD50 Light dose at which 50% of the population dies 5x105 Jm-­‐2 m Exponent of light probability function 1.45 -­‐ Aquatic Sciences (1995) Hmin Minimal water column depth 2 m Wittmann et al. (2008) Hmax Maximal water column depth 39 m Wittmann et al. (2008) -­‐ Aquatic Sciences (1995) McMahon (pers.comm.) 65
-­‐ Figures -­‐ Figure 28. Lake Tahoe location, bathymetry and location of meteorological stations (black), Marla Bay (red), region of particles release (blue star), and shore line sections (green): (1) Emerald Bay, (2) Rubicon Bay, (3) Tahoe City, (4) Carnelian Bay, (5) Crystal Bay, (6) Harbors, (7) Glenbrook Bay, and (8) South Lake. Figure 29. Sketch of particle resuspension, and sources and initiation of particle transport. Red dots represent Asian clam larvae. 66
Figure 30. Density distribution of Asian calm adult population in 2005. Discrete sampling locations (A) and interpolated map of Marla Bay (B). Figure 31. Fraction of Marla Bay that resuspends due to normalized bottom shear velocity u* induced by waves (blue), currents (green) and both waves and currents (red) Figure 32. Horizontal distribution of resuspension frequency in Marla Bay induced by waves (A), currents (B), and combined wave and currents (C). Black lines mark depth contours for orientation. 67
Figure 33. Larvae population temporal dynamics integrated over Marla Bay at the sediments (line) and resuspension pulses (vertical bars) in function of settling velocity ws=10-­‐3 ms-­‐1 (A) and ws=10-­‐4 ms-­‐1 (B).} Figure 34. Dominant wind directions and speed in Marla Bay. 68
Figure 35. Temporal variation of wind speed (blue) and direction (green) in Marla Bay. Figure 36. Spatial variation of surface current in Marla Bay in EW (upper panels) and NS (lower panels) direction at times of the day when particles are released. 69
Figure 37. Spatial variation of bottom current in Marla Bay in EW (upper panels) and NS (lower panels) direction at times of the day when particles are released. Figure 38. Mean EW velocity (ms-­‐1) along western edge of Marla Bay. 70
Figure 39. Risk map of Asian clam colonization: Horizontal distribution of final particle position (blue) and potential colonization sites (red) (A) and probability of larvae colonization (B). A preliminary understanding of clam filter feeding rates, excretion rates and subsequent impact to phytoplankton and filamentous algae Background to the Asian clam feeding experiment Pilot research in late summer 2008 suggested that Asian clams may be linked to the algal blooms of Zygnema in Marla Bay (Wittmann et al. 2008). It is unclear however if clams may impact the structure of seston (phytoplankton, sediment, and detritus) within the lake water column. In other ecosystems, invasive clams have altered the dominant zooplankton population by out-­‐competing them for algal resources or preying on early life stages (Kimmerer et al. 1994). The Asian clam has been found to have variable filtration rates of seston depending on the temperature and food concentration in the water (Lauritsen 1986). While Lake Tahoe generally is characterized by low algal biomass the nearshore environment, these biomasses are variable dependent on location (TERC 2008). Asian clams have demonstrated the ability to cause multiple ecosystem impacts on nutrient recycling and primary productivity (Hakenkamp and Palmer 1999, Lauritsen 1986). Nutrient cycling can be altered at the benthic-­‐pelagic interface through Asian clam pedal feeding (from sediments), and nutrient excretion and active burrowing within sediments (Vaughn and Hakenkamp 2001). Pedal feeding may be more utilized than previously thought and it allows juvenile clams to grow faster than with filter feeding alone (Vaughn and Hakenkamp 2001). The impacts of filter feeding are most severe when the filter feeding bivalve has a high relative biomass (Vaughn and Hakenkamp, 2001) or exists in high densities (Hakenkamp, 2001). Factors that influence the filtration rate are bivalve size, water 71
temperature and food concentration. The purpose of this study is to determine the filtration rates of the Asian clams found in Lake Tahoe in order to understand the potential impact on Lake Tahoe phytoplankton communities, to look at the impacts of temperature and Asian clam size on the excretory rates of Asian clam, and finally to assess the impacts of Asian clam excretory materials on algal growth. Methods All filtration rate experiments were carried out at the UC Davis Tahoe City Field Station and the Tahoe Environmental Research Center in Incline Village, NV. Water was collected from a depth of 1-­‐1.5 meters at Tahoe City, CA and pre-­‐filtered through an 80 µm mesh screen. 9.5 liters of lake water was placed into each of the 44 individual 20 L clam containment chambers used during the study. All chambers were acid washed with 0.1 N HCl and copiously rinsed with deionized water prior to use. Sediments and clams were collected from Marla Bay, NV and clams sorted into two size classes, large and small (large mean ~18mm: small mean ~10 mm). Clams were measured with a digital caliper and placed in a chamber, one clam per chamber. Each size class used 20 of the 20 L chambers; 10 with 5 cm of sediment and 10 without sediment. Four chambers served as controls, 2 with sediment and 2 without. In addition, two chambers for each treatment (S-­‐small clam without sediment, SS-­‐small calm with sediment, L-­‐
large clam with without sediment, SL-­‐large clam with sediment) and 1 control chamber (without sediment) were utilized for algae samples. Chlorophyll readings were obtained with a 10 AU fluorometer. Three 5-­‐mL samples of water were collected from each chamber after quickly stirring the water with the pipette tip and measured for in vivo fluorescence. Dissolved oxygen (mg/L and % saturation), conductivity, and temperature were measured with a YSI-­‐85 prior to each time interval for three selected chambers in different treatments: S1, SL1 and C1. Samples of filtered (80 µm mesh) lake water preserved in Lugol’s solution were saved for algae identification. As the in vivo samples were instantly measured for the selected chambers, 100 mL was also collected from each chamber and filtered through a Whatman GF/C glass fiber filter. The TERC chlorophyll a extraction procedure (Appendix 1) was followed to obtain values. Extractions were performed 3-­‐6 days after sample collection. A linear regression comparing chlorophyll a concentrations to the mean fluorometer reading for the corresponding chamber/time point was used to convert all fluorescence measurements to chlorophyll a concentration (R2 = 0.32). Visual feeding/burrowing observations were recorded before and after each sampling interval. If the siphon of the clam was protruding from the shell or the clam was clearing feeding, it was recorded as feeding. If the siphon was not visible and the shell was completely closed it was recorded as not feeding. Also, it was noted if the clams in sediment were burrowed or not. The experiment was conducted over a 60 h period measuring fluorescence at 12 hour intervals, at 7pm and 7am. Clams were collected the day of the experiment and allowed to acclimate 4-­‐7 hours based on their treatment (sediment or no sediment) in fresh lake water. The natural light cycle was extended by 2 hours with artificial overhead lights from 8:30 pm-­‐
10:30 pm as measurements were taken. After the experiment, clams were collected and oven-­‐
72
dried at 60 C for 11 days. Dry flesh weight (grams) and total dry weight were measured after 11 days of drying. ANCOVA (analysis of covariance) was used in S-­‐Plus statistical software to determine if the difference in the filtration rate slopes of the control vs. large clams (with sediment) and control vs. small clams (with sediment) were statistically significant. Results and Discussion Pooling the DO, conductivity and temperature measurements from the chambers, DO (%) ranged from 62.7%-­‐55.7%, DO (mg/L) 5.71-­‐5.3, conductivity (uS/cm) 89.7-­‐78.6 with measurements generally decreasing over time. Water temperature was initially measured at 22.5 °C immediately after distribution, but during the duration of the experiment, ranged from 20-­‐18.5 °C and followed daily temperature variation. Figures 45-­‐46 show the pooled filtration data (chlorophyll a concentration as a function of time) from the chambers in each treatment (S, SS, L, SL) and the two control groups. Large clams with sediment-­‐SL, mean length = 17.9 mm, were able to reduce algal biomass by 56%, and filter 9.6 L (2.5 gal) of water, in 24 hours or 394 mL/hour. A line of best fit gives an R2 of 0.52 for the SL group, indicating a fairly linear relationship. Large clams without sediment-­‐L reduced algal biomass by 81% and filtered the algae within the same amount of time (9.6 L/24 hr). Chlorophyll a concentrations at each time point vary less in the L group when compared to SL group. Small clams with sediment-­‐SS, mean length = 10.6 mm, decreased algal biomass by 54%, but took double the time (197 mL/hr) compared to large clams. The SS group data also decreases linearly, R2 = 0.62. Chlorophyll a
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Figure 46. Mean chlorophyll a concentration as a function of time in a 20 L chamber for control with no sediment. Algal biomass in the control with sediment (CS) reduced by 29% in 24 hours and 33% in 48 hours. In the control without sediment, algal biomass decreases were 17% and 18% for 24 and 48 hours respectively. ANCOVA results suggest that differences between treatments and control is significant (Tables 7 – 9). Control-­‐CS vs. large clams-­‐SL had a p-­‐value of 0.000 (Table 7). Control-­‐CS vs. small clam-­‐SS had a p-­‐value of 0.049, using a 95% confidence interval (Table 76
8). Further, the slopes of large-­‐SL vs. small clams-­‐SS were compared (Table 9) and were also found to be statistically different (p=0.000). Statistical analyses of data from clams without sediment were not compared due to a lack of linearity in the data of large clams, and irregular data of small clams. Control-­‐CS vs. Large-­‐SL Summary Value Std. Error T value Pr(>|t|) (Intercept) 0.1870 0.0068 27.3251 0.0000 Time 0.0029 0.0003 -­‐9.9210 0.0000 Factor (treatment) -­‐0.0034 0.0068 -­‐0.5017 0.6168 Time:factor (treatment) -­‐0.0015 0.0003 -­‐5.1649 0.0000 Residual standard error: 0.0382 on 115 degrees of freedom Multiple R-­‐Squared: 0.54 F-­‐statistic: 45.24 on 3 and 115 degrees of freedom, the p-­‐value is 0 Table 7. ANCOVA results for Control with sediment (CS) and Large Clam treatment with clam size versus control as treatment factors. Control-­‐CS vs. Small-­‐SS Summary Value Std. Error T value Pr(>|t|) (Intercept) 0.1904 0.0094 20.1798 0.0000 Time -­‐0.0014 0.0003 -­‐4.3130 0.0000 Factor (treatment) -­‐0.0041 0.0103 -­‐0.4012 0.6888 Time:factor (treatment) -­‐0.0007 0.0004 -­‐2.0100 0.0460 Residual standard error: 0.02984 on 176 degrees of freedom Multiple R-­‐Squared: 0.58 F-­‐statistic: 81.11 on 3 and 176 degrees of freedom, the p-­‐value is 0.046 Table 8. ANCOVA results for Control with sediment and small clam treatment with clam size versus control as treatment factors. Large-­‐SL vs. Small-­‐SS Summary Value Std. Error T value Pr(>|t|) (Intercept) 0.1863 0.0051 36.8200 0.0000 Time -­‐0.0021 0.0002 -­‐12.1516 0.0000 Factor (treatment) -­‐0.0027 0.0078 -­‐0.3473 0.7287 Time:factor (treatment) -­‐0.0023 0.0004 -­‐5.4564 0.0000 Residual standard error: 0.03578 on 235 degrees of freedom Multiple R-­‐Squared: 0.54 F-­‐statistic: 93.03 on 3 and 235 degrees of freedom, the p-­‐value is 0 77
Table 9. ANCOVA results for comparison of large clams and small clams with size and time as treatment factors. Clams were observed for feeding and/or burrowing before and after each sampling period, with a total of 11 observation records. Of the 10 replicates of small clams without sediment (S), percentage of positive feeding observations ranged from 36.4%-­‐100% with and mean of 77.7%. Large clams without sediment (L) ranged from 9%-­‐100% with a mean of 50% positive feeding. All replicates of small clams with sediment (SS) were burrowed 100% of the experiment, no feeding observations were possible. Three of the large clams with sediment (SL) were observed unburrowed on multiple occasions; the other seven were burrowed 100% of the experiment. Asian clam filtration rates observed in the literature vary considerably. Shell length correlates with filtration rate (Lauritsen 1986) which was also been observed in this experiment. Rates for clams 20 mm in length have been reported at a mean of 11 mL/h (chlorophyll a) and a maximum rate of 816 mL/h (Lauritsen 1986). Large clams (mean length = 17.9 mm) in this study filtered chlorophyll a at a rate of 394 mL/h which is within the range of clams observed in the Lauritsen (1986) study. Small clams (mean = 10.6 mm) filtered at a rate of 197 mL/hr, which is still higher than studies using clams mean length of 20 mm. While filter feeding is possible burrowed and unburrowed, Asian clam preference to burrow was demonstrated and the treatment with sediment reflected natural conditions and therefore more realistic filtration rates. There was greater variation in the data for large clams with sediment than without. This was expected as benthic food sources were made available. Sediments were not autoclaved, as in other studies, allowing both bacteria and nutrients to be present in the test chambers. The presence of bacteria in the sediments may explain the slight differences in the controls. The control with sediment had a greater decrease in chlorophyll a values than the control without sediment. If Asian clams are able to continue spreading in Lake Tahoe, they may be able to impact the algal biomass in the lake littoral zone. Filtration rates are moderately high and clams are filter feeding whether burrowed and unburrowed. We suspect that clams will likely not have a large biomass relative to water volume in Lake Tahoe. However, there may be local decreases in algal biomass in near shore areas with dense clam populations. The distribution and amount of algal biomass in the near shore will also depend on the physical currents and water circulation patterns that transport water between the pelagic and littoral regions. 78
ACKNOWLEDGEMENTS This research represents a wholly collaborative effort between research institutions: University of California Davis Tahoe Environmental Research Center, the Aquatic Ecosystems Laboratory of the University of Nevada Reno, and the University of Granada, and federal, state, and regional agencies: Tahoe Regional Planning Agency, United States Fish and Wildlife Service, Lahontan Regional Water Quality Control Board, Nevada Division of State Lands, Nevada Department of Environmental Protection, Tahoe Water Suppliers Association, California Department of Fish and Game, California State Parks and Recreation, Nevada Department of Wildlife, Tahoe Resource Conservation District, and the California State Lands Commission. Coordination between the research team and the agency representatives occurred through the Asian Clam Working Group (ACWG). This research has been presented to local stakeholders at Lake Tahoe, and at regulatory and academic meetings both nationally and internationally. This research was funded by the Nevada Division of State Lands, US Pacific Southwest Research Station throught the Southern Nevada Public Lands Management Act, the University of Nevada Reno, and the Lahontan Regional Water Quality Control Board. Andrea Hoyer was supported by a PhD grant (Formación del Profesorado Universitario) from the Spanish Government. We would like to thank the following for their assistance in collecting, analyzing, and discussing the information presented in this manuscript: Anne Liston (UCD), Veronica Edveeringsam (UCD), Patty Arneson (UCD), Collin Strasenberg (UCD), George Malyj (UCD), Joe Sullivan (UNR), Marcy Kamerath (UNR), Brant Allen (UCD), Katie Webb (UCD), Raph Townsend (UCD), Dr. Charles R. Goldman (UCD), Dr W. Fleenor (UCD), Dr. Simon Hook (NASA-­‐JPL), Dr. Andrew Tucker, Dr. Craig Williamson, and Dr. James Oris (Miami University in Ohio), Dr. Lars Anderson (USDA ARS Laboratory), Brian Shuter (University of Toronto), Dr. Almo Cordone (California Department of Fish and Game-­‐retired). The Williamson Laboratory at Miami University in Ohio and Dr. Andrew Tucker were instrumental in collecting and sharing their information related to light attenuation in the lake. 79
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