Evaluation of salmon and alder as drivers of nutrient availability … · 2013. 12. 18. · Togiak National Wildlife Refuge – Salmon, alder, and lake productivity, September 2013

Evaluation of salmon and alder as drivers of nutrient availability and lake productivity in

southwestern Alaska

2012 Progress Report

Feng Sheng Hu, Jennifer Fraterrigo, Daniel Schindler, Patrick Walsh, Denise Devotta, Stacey Lowe, Tim Sands

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Togiak National Wildlife Refuge Dillingham, Alaska;

University of Illinois at Urbana-Champaign Urbana, Illinois;

University of Washington Seattle, Washington;

September 2013

Citation: Hu F.S., J. Fraterrigo, D. Schindler, P. Walsh, D. Devotta, S. Lowe, T. Sands. 2013.

Evaluation of salmon and alder as drivers of nutrient availability and lake productivity in southwestern Alaska, 2012 progress report. U.S. Fish and Wildlife Service, Togiak National Wildlife Refuge, Dillingham, Alaska.

Keywords: Oncorhynchus, Alnus, aquatic productivity, nutrient availability, Togiak National Wildlife Refuge, southwestern Alaska Disclaimer: The use of trade names of commercial products in this report does not constitute endorsement or recommendation for use by the federal government.

Togiak National Wildlife Refuge – Salmon, alder, and lake productivity, September 2013 U.S. Fish and Wildlife Service

Evaluation of salmon and alder as drivers of nutrient availability and lake productivity in southwestern Alaska

2012 Progress Report

Feng Sheng Hu, Jennifer Fraterrigo, Daniel Schindler, Patrick Walsh, Denise Devotta, Stacey Lowe, Tim Sands1

Abstract We completed the third year of work in a four-year project to investigate the relative importance of nutrients derived from salmon and alder on lake productivity in southwestern Alaska. We installed water quality and quantity monitoring equipment at 13 lakes on Togiak National Wildlife Refuge. We collected and analyzed over 200 water samples in summer and fall 2010-2012 for a battery of biological and chemical attributes. We monitored stream discharge in summer and fall at 26 streams entering the study lakes in order to estimate lake water budgets. We performed annual aerial salmon surveys at all lakes and estimated run size in each. We have updated an existing landcover map to refine our estimate of alder cover in the study area. We refined estimates of lake volume for all study lakes.

Oncorhynchus, Alnus, aquatic productivity, nutrient availability, Togiak National Wildlife Refuge, southwestern Alaska

Introduction

Understanding the direction, magnitude and impact of major nutrient fluxes across ecosystem boundaries is fundamentally important in ecosystem studies (Nakano and Murakami 2001, Zhang et al. 2003, Chaloner et al. 2004). Nutrients released by N-fixing plants, including actinorhizal (Frankia-nodulated) alder (Alnus spp.), can greatly impact nutrient dynamics and productivities of terrestrial and aquatic habitats (Baer et al. 2006).

1 Authors: Feng Sheng Hu, University of Illinois, 505 South Goodwin Avenue, Urbana, IL 61801. (217) 244-2982, [email protected]. Jennifer M. Fraterrigo, University of Illinois, 1102 South Goodwin Avenue, Urbana, IL 61801. (217) 333-9428, [email protected]. Daniel Schindler, University of Washington, School of Aquatic and Fishery Sciences, Box 355020, Seattle WA 98195 (206) 616-6724, [email protected]. Patrick Walsh, U. S. Fish and Wildlife Service, Togiak National Wildlife Refuge, P.O. Box 270, Dillingham, AK 99576. (907) 842-1063, [email protected]. Denise Devotta, University of Illinois, 505 South Goodwin Avenue, Urbana, IL 61801. (217) 244-9871, [email protected]. Stacey Lowe, U. S. Fish and Wildlife Service, Izembek National Wildlife Refuge, P.O. Box 127, Cold Bay, AK 99571. (907) 532-2549, [email protected]. Tim Sands, Alaska Department of Fish and Game, P.O. Box 230, Dillingham, AK 99576, (907) 842-5227.

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Togiak National Wildlife Refuge – Salmon, alder, and lake productivity, September 2013 U.S. Fish and Wildlife Service A number of previous studies have documented the importance of alder-dominated vegetation as a source of dissolved nutrients to adjacent aquatic systems. For example, Goldman (1961) found elevated concentrations of nitrate and ammonium and stimulated phytoplankton growth (chlorophyll a) in Castle Lake, California, as a result of N fixation from alder stands in the lake watershed. Similarly, Compton et al. (2003) and Volk et al. (2008) found dissolved stream N concentrations are positively correlated with the abundance of alder in the watershed vegetation in Oregon and coastal Washington. Pacific salmon (Oncorhynchus spp.) represent another classic example of nutrient fluxes across ecosystem boundaries (Gende et al. 2002, Naiman et al. 2002, Chaloner et al. 2004). Salmon transfer marine-derived nutrients (MDN) from oceans to freshwater systems when they migrate upstream to spawn in freshwater habitats. In regions where both are abundant, salmon and alder may interact with each other to elevate nutrient and productivity levels of freshwater systems. At coarse spatial scales, alder often grows in salmon-spawning areas, as alder favors open, riparian sites for establishment. Where alder-fixed N is more abundant, riparian vegetation is less dependent on salmon-derived N and alder growth increases as a result of using MDN from salmon (Helfield and Naiman 2001, Edmonds and Mikkelsen 2006). The importance of such nutrient subsidy interactions between alder and salmon may increase in riparian areas where disturbance creates openings conducive to rapid alder colonization (Edmonds and Mikkelsen 2006). Southwestern Alaska supports the greatest salmon runs in the world. Marine-derived nutrients through these salmon runs play a key role in nutrient cycling and productivity of terrestrial and aquatic ecosystems (Helfield and Naiman 2001, Schindler et al. 2003, Schindler et al. 2005 ). In this region, alder is a prevalent constituent of plant communities on mountain slopes and riparian zones. Pollen and geochemical analyses at a lake indicate that when extensive alder thickets similar to those on the modern landscape became established around 8000 years, N availability and lake productivity increased dramatically (Hu et al. 2001). However, little is known about the effects of alder on the nutrient regime and productivity of lakes today. As a pilot study leading to the project described in this study plan, Devotta (2008) analyzed water samples from seven lakes in the Togiak drainage for a suite of nutrient and productivity variables and assessed the relationships of these variables with the abundance of alder in watershed vegetation. Results indicate that watershed alder cover significantly enhances nutrient availability and lake productivity. Furthermore, the amount of alder-derived nutrients is probably much greater than that of MDN in the Togiak drainage (see below). This finding has important implications for maintaining the health and integrity of ecosystems in the Togiak National Wildlife Refuge. However, Devota’s results are based on a small dataset collected from seven sites during several days. In addition, quantitative salmon-density information is unavailable for these lakes, making it impossible to rigorously evaluate the relative importance of alder and salmon as nutrient sources to the freshwater ecosystems of the Togiak Refuge. Differences in watershed characteristics that affect nutrient transport may also be important for understanding the influence of alder on lake productivity, but have not yet been investigated.

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Togiak National Wildlife Refuge – Salmon, alder, and lake productivity, September 2013 U.S. Fish and Wildlife Service The overall goal of this project is to determine the relative roles of alder (Alnus viridus) and salmon (Oncorhynchus spp.) in controlling nutrients and productivity in aquatic ecosystems in southwest Alaska. To achieve this, we must obtain lake nutrient and productivity data from a suite of lakes selected to represent a wide range of alder and salmon gradients. Each of these lakes will be sampled twice annually during the ice-free season for four years, the first field season having been completed over the summer of 2010. Watershed features will be characterized to examine the role of abiotic characteristics in modulating the relationship between alder and lake productivity. Our working hypothesis is: Alder provides (or makes biologically available) greater amounts of N and P to freshwater systems than salmon at the watershed scale. Where alder is abundant, the nutrient regime and productivity of a lake have a greater buffering capacity against salmon harvests. To test this hypothesis, we are conducting GIS, biogeochemical, and limnological research at 13 lakes in the Togiak Refuge. These lakes were selected to include broad gradients of watershed alder coverage and salmon abundance. Our specific objectives are: Objectives

1. Refine estimates of alder coverage within each lake watershed using multi-date

remote sensing techniques to achieve an accuracy level of 70% 2. Quantify productivity components from lake water including chlorophyll-a,

bacterioplankton counts, and zooplankton biomass. 3. Infer/estimate lake primary productivity on the basis of chlorophyll-a concentration

and biogenic-silica accumulation in sediment traps, and assess the effects of primary productivity on other trophic levels on the basis of bacterioplankton and zooplankton abundance.

4. Obtain water samples and velocity profiles of inlet streams to lakes for nutrient loading estimates. Install stream stage gauges in streams for high-resolution data on stream inputs.

5. Install sediment traps for collection of sediment and estimation of diatom productivity, as well as isotope data for inferring relative importance of salmon- and alder-derived nutrients.

6. Install lysimeters in soils under alder thickets to assess the contribution of N and P derived from alder and to estimate potential N and P export to surface waters.

7. Estimate salmon spawner density in each lake based on nitrogen isotopes in lake surface sediments, in sediments annually produced during the course of the study, and via aerial salmon counts.

8. Determine the relative importance of watershed alder coverage, salmon abundance, and watershed and lake-morphometric variables for lake nutrient dynamics and productivity.

Justification The importance of salmon to humans as a food and recreational resource has been long understood, but there is now a growing awareness of the need to understand the

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Togiak National Wildlife Refuge – Salmon, alder, and lake productivity, September 2013 U.S. Fish and Wildlife Service ecosystem services salmon provide. The Bristol Bay-Chignik Area Planning Workgroup (2005) addressed this need during a strategic plan for subsistence management of fishery resources. The workgroup identified and prioritized 23 information needs, two of which were: Determine salmon escapements by lake/river system needed to sustain ecosystem functions (rank 11 out of 23), and Relate historic salmon harvests to current productivity levels of river/lake systems (rank 19). When established, Togiak Refuge was charged by the U.S. Congress to undertake scientific studies and implement management actions designed to conserve biodiversity, and the refuge specifically identified the need to focus attention on salmonids (Alaska National Interest Lands Conservation Act, P.L. 96-487, as amended). In keeping with this charge, Togiak Refuge established an objective to Evaluate the effects of the commercial harvest on resident and anadromous fish populations on the Togiak Refuge (USFWS 1990). This need was validated by an independent group of experts when the Togiak Refuge underwent a programmatic review (Heglund and Taylor 2005). The panel provided a priority evaluation of "High" to this objective, and further recommended: The refuge should collaborate and support studies to address the roles and relative importance of marine-derived nutrients (i.e., salmon) to freshwater and riparian communities and including how marine derived nutrient data may help develop ecological escapement goals for refuge watersheds.

Overharvest has caused many Pacific salmon populations to become extinct or be at the risk of extinction at the southern end of their range (Gustafson et al. 2007). In the Togiak Refuge, harvest levels of salmon for some individual watersheds exceed 75% of the annual return, and it is questionable whether this level of harvest is sustainable. Decreased salmon populations and associated MDN reductions may lead to dramatic negative impacts on ecosystems in these regions, including decreases in ecosystem productivity and biodiversity. Alder is a prevalent constituent of the plant communities in some of these regions, especially where natural (e.g., fires, avalanches) disturbances are common. Alder N fixation and the coupled P increase have potential to serve as a key alternative subsidy for maintaining ecosystem health and integrity. Thus it is important to improve our understanding of the relative importance of salmon and alder as ecosystem nutrient drivers.

Study area and background

Overview The study area occurs on or near the Togiak Refuge, a 1.6 million ha federal conservation unit in southwest Alaska. Located at the junction of the Bristol and Kuskokwim Bays of the Bering Sea, the study area climate can be considered sub-Arctic maritime near the coast, transitioning to sub-Arctic continental toward the interior. Temperatures range from average daily low and high of -11.9 and -5.8 o

C in December, the coldest month, to 9.1 and 16.1o C in July, the warmest month in Dillingham, AK, the nearest weather station (climate data averaged 1971-2000, National Oceanic and Atmospheric Administration 2008). Annual precipitation averages 64.5 cm and snowfall averages 210.6 cm.

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Togiak National Wildlife Refuge – Salmon, alder, and lake productivity, September 2013 U.S. Fish and Wildlife Service The Ahklun Mountains occupy the central portion of the study area while the Nushagak and Kanektok River lowlands occur to the northwest and southeast (Fig. 1). The modern-day landscape, and thus the distribution of both alder and salmon within this landscape, is strongly influenced by glacial activity in the late Pleistocene. Small alpine glaciers remain in the northeastern portion of the study area, and provide minor water inputs to a relatively small number of lakes (Walsh et al. 2007).

Figure 1. Study area topography. Study area land cover The study area has been mapped into 25 separate plant communities, which occupy ~84% of the total area, while the remaining 16% is covered by unvegetated cover types, such as snow/ice, water, bare rock/gravel, and cloud (Collins 2003). The predominant plant communities are upland herb tundra, lichen, and low or dwarf shrub communities from 11 community classes, and compose ~49.5% of the whole. The second-most predominant communities are the tall shrub types, and compose ~19.4% of the total study area. Of these, alder (Alnus) and mixed alder/willow (Salix) communities account for ~14.5% of the study area, and occur commonly on the base of mountain slopes (Fig. 2). Four wetland community types account for ~12.2% of the total, and four deciduous forest communities covering ~2.4% of the total area occur predominantly in riparian zones. Spruce (Picea) and spruce/deciduous forest types account for less than 1% of the total area, and are restricted to the southeastern quarter of the study area.

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0 20 4010 Kilometers

Figure 2. Alder and mixed willow/alder coverage on Togiak Refuge. Shaded areas are the principal watersheds composing the study area. Watersheds Togiak Refuge contains all or most of 11 river watersheds as well as portions of five adjacent watersheds (Fig. 3). Including tributaries, the area includes all or portions of 35 major rivers and hundreds of small streams (USFWS 1990). The area includes 175 lakes >10 ha in area, including 53 lakes over 100 ha (Fig. 4). Median lake size is 54 ha, and they vary in elevation above sealevel from ~30 m to >400 m. The lakes and streams provide spawning and rearing habitat for the five Pacific salmon species.

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Togiak Wood

Igushik

Goodnews

Kanektok

Snake

Figure 3. Major river watersheds of the Togiak Refuge vicinity. Study area river watersheds are named. Coastal areas without significant river watersheds are colored tan.


.

Figure 4. Major lakes of the Togiak Refuge vicinity.

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Methods

Lake selection Of the ~175 lakes potentially available within the Togiak Refuge vicinity, we used the following criteria to select 13 lakes:

1. Lakes must be >10ha in surface area. 2. Lakes must be geographically widespread throughout the river watersheds of

Togiak Refuge. 3. Lakes must cover a broad elevation gradient. 4. Lake watersheds must be wholly encompassed by the Togiak Refuge

landcover map (Collins 2003). 5. Lakes must cover a broad gradient of salmon density. 6. Lake watersheds must cover a broad gradient of alder cover. 7. A minimum sample size of 12 lakes was established, as this is the approximate

limit of logistical feasibility. 8. Lakes with existing records of research, including limnological, bathymetric,

fisheries, and other studies, were preferable to those with no investigation histories.

We selected 12 lakes that are accessible by salmon and one control lake that is inaccessible by salmon (Table 1, Fig. 5). The control, High Lake, is isolated from salmon due to a waterfall. Additionally, Nagugun, which although is accessible to salmon, is difficult to reach (due to stream gradient), and thus receives little use by salmon.

Table 1. Physical characteristics and landcover data for study lakes.

Lake

Lake watershed

ha

River watershed

% alder cover

alder in watershed

ha %MDN Surface area ha

Volume (ha m)

Elevation m

Max depth

m Nenevok 7,868.4 Togiak 18.2 1,433.3 9.6 250.1 3,218.2 351 32 Nagugun 4,409.5 Togiak 20.6 909.5 0 534.5 12,745.9 285 58 High 8,305.3 Togiak 22.4 1,856.3 0 626.8 14,219.4 262 56 Togiak 124,098.9 Togiak 26.6 32,979.0 21 3901.5 221,117.5 67 146 Gechiak 7,637.9 Togiak 41.5 3,172.1 12.8 426.1 2,377.6 101 22 Ualik 16,455.6 Igushik 39.3 6,459.4 36.1 3916.2 108,877.1 16 18 Snake (Nunavaugaluk) 31,224.0

Snake 45.4 14,167.2 27.6 8049.2 439,750.5 10 160

Kagati/Pegati 26,594.6 Kanektok 8.0 2,138.2 21 938.7 17,274.9 317 51 Goodnews 22,101.7 Goodnews 28.0 5364.1 21 437.3 7,545.9 123 40 Ohnlik 3,296.4 Kanektok 13.2 436.1 3.3 166.3 3,842.7 351 43 Little Togiak 8,256.9 Wood 51.6 4,260.7 8 836.4 20,779.0 12 60 Ongivinuk 3,152.4 Togiak 49.5 1,560.7 122.7 898.7 163 12 Pungokepuk 6,867.6 Togiak 30.3 2,080.6 600.5 1,957.8 69 17

Note: Surface area, volume, elevation, and maximum depth from Lowe et al. 2013.

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Snake

Ualik

Togiak

Little Togiak

Kagati/Pegati

High Lake

Nagugun

Gechiak

Pungokepuk

Goodnews

NenevokOhnlik

Ongivinuk


Togiak National Wildlife Refuge

Figure 5. Location of study lakes (dark blue) and associated lake watersheds (light blue) at Togiak National Wildlife Refuge. Study lakes range from 10 – 351 m above sealevel. Lake watersheds were delineated in ArcGIS 9.0 using digital elevation models (30-m resolution) and hydrologic layers derived from the Digital Chart of the World for Alaska (ESRI 1996). Alder cover estimates from the Togiak Refuge landcover map (Collins 2003) were refined using a multi-date approach to classifying landcover in Erdas Imagine. In addition to the original Landsat 5 image from August 1989 used in the existing landcover classification, a Landsat 7 layer from June 2009 was included. Because of differences in color and timing of fall senescence for willow and alder, more unique signatures were achieved through this method. Individual bands in each layer were radiometrically corrected and stacked; NDVI layers of each date were also created and included. The final composite image included 14 bands, with 6 color bands and NDVI data for each date. A supervised classification using the maximum likelihood classifier was performed using data from the training sites of the original Togiak Refuge landcover study (Collins 2003). Alder area was determined for each study lake watershed by summing two alder-dominated landcover classes from the Togiak Refuge using the revised landcover map (Tall Shrub

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Togiak National Wildlife Refuge – Salmon, alder, and lake productivity, September 2013 U.S. Fish and Wildlife Service Alder and Tall Shrub Mixed Alder and Willow). Percent alder coverage ranged from 1.5 – 40.1 (Table 1).

Salmon density Following the methods of Schindler et al. (2005), we used N isotope ratios (δ15N) from sediment cores to estimate relative abundance of salmon among lakes (Table 1, Fig. 6). Sediment cores were available from all but two selected lakes (Schindler et al. 2007, Walsh et al. 2006). An average of two measurements of surface sediments collected at depth of ~ 0.25 and 0.5 cm were used to restrict estimates to the most recent 10-25 years. The proportion of salmon-derived N in a sample was calculated as:

PNsalm = (δ15Nsed - δ15Nws)/( δ15Nsalm - δ15Nws) where: δ15Nsed is the δ15N of the sediments from a given salmon nursery lake representing a mixture of nitrogen loaded from salmon and from the watershed, δ15Nws is the δ15N from watershed sources, δ15Nsalm is the δ15N of salmon tissue (values taken from Johnson and Schindler 2008). Although salmon carcasses release nitrogen enriched in the 15N isotope, 15N also occurs at lower proportions in non-salmon systems due to atmospheric deposition. Thus, in developing a model to estimate salmon density based on δ15N levels, we subtracted the background 15N. We used the δ15N values from local non-salmon lakes to estimate Nws..

Because there is an east-to-west variation in watershed δ15N, we used (1) Grant Lake, a non-salmon lake ~40 km east of the study area, as the δ15Nws level for lakes in the Wood, Igushik, Togiak, and Snake River watersheds; and (2) Nagugun Lake (located in the Kanektok watershed), a lake with little to no salmon use, as the as the δ15Nws level for lakes in the Kanektok and Goodnews River watersheds.

0.0

0.1

0.3

0.4

0.0 0.1 0.3 0.4

Hole

Ohnlik

Nagugun

Gechiak

Nenevok

Little TogiakUalik

Kagati/Pegati

Goodnews

Snake

HighTogiak complete

Percent__MDN

Per

cent

_ald

er_c

over

Figure 6. Study lakes by percent alder cover and percent marine derived nutrients.

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Togiak National Wildlife Refuge – Salmon, alder, and lake productivity, September 2013 U.S. Fish and Wildlife Service Additionally, at the conclusion of all field work, we will estimate yearly salmon population density by analyzing sediment samples from funnel traps in each lake for δ15N to estimate salmon abundance (e.g., Finney et al. 2000; Schindler et al. 2005; Selbie et al. 2009). We expect that this approach will yield results generally consistent to with those from surface-sediment analysis (Fig. 6). Analysis of δ15N will be performed on an elemental analyzer interfaced with a Europa Hydra 20/20 Isotope Ratio Mass Spectrometer (PDZ-Europa, Crewe, UK). As a secondary, and more direct method of estimating salmon population density, we performed aerial salmon surveys annually in each of the study lakes supporting spawning salmon (n=11). Aerial surveys were performed from small helicopters by individuals with experience in aerial salmon surveys following the methods of the Alaska Department of Fish and Game (2002). Lake shorelines and major tributaries to each lake were surveyed, and both live and dead salmon were counted. In areas where salmon are too numerous to count individuals, the numbers were estimated. Surveys took place in late August to mid-September, at which time the majority of sockeye, king, chum, and pink salmon runs had ended, but prior to decomposition of all carcasses. We expect that this approach will produce results consistent with that of the nitrogen isotope methods, but in cases in which they disagree, we will rely on the aerial surveys. Nutrients and productivity Most of our study lakes were previously surveyed for bathymetry, mean depth, maximum depth, Secchi depth, lake water pH, and conductivity (MacDonald 1996). Detailed information on river/stream discharge rates is also available at most of these sites. We used this information to guide our water sampling, and will use it to calculate lake-water residence time that may affect inter-annual nutrient dynamics, and assess the effects of watershed hydrological processes on lake nutrients and primary productivity. We obtained water samples from lakes and inlet streams twice annually: early summer (at ice-out and before the onset of the salmon run), and fall (following the salmon run). During each sampling trip in 2010, we collected two water samples from each lake in 1 L containers. One sample was taken from the mouth of an inlet stream or near the lakeshore where the lake was approximately 12 m deep, and the second was taken where the lake was approximately 18 m deep, and represented an integrated Secchi-depth sample (we divided the Secchi depth into three equal intervals, and Van Dorn samples from the three depths were combined to produce a single sample). In 2011 and 2012, we collected a single water sample from each lake during each sampling trip where the lake was ~18 m deep. Within each watershed, we also collected water samples from lower-order streams (first to third order) that flow into the study lake and are not being used by salmon as spawning habitat. We selected streams using the following approach: from all inlet streams of the study lakes, we first identified pairs containing streams with watersheds of similar size and then selected the pairs which also contained watersheds of similar slope and different

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Togiak National Wildlife Refuge – Salmon, alder, and lake productivity, September 2013 U.S. Fish and Wildlife Service amounts of alder cover. To ensure that at least three streams were identified for every lake, we then selected additional streams from each lake watershed that were accessible and whose watershed represented a large percentage of the entire lake watershed. We delineated individual stream watersheds for each lake and calculated watershed slope using ArcMap 9.3.1. Stream water samples were obtained for chemical analysis twice annually, on approximately the same day that we sampled the corresponding lake. During each sampling event, we collected one water sample from each stream in a 500 ml container. We computed stream flow for each sampled stream using stream height (stage) and stage-discharge relationships. We recorded stream height, water temperature, and air temperature every 2 hours during the open water season using a stilling well fitted with a water-level data logger (WTHR-1000, Intech Instruments Ltd., Christchurch, New Zealand). Cross-sectional profiles of at least ten equally spaced measurements of stream velocity and depth were taken twice during the season and were used to calibrate stage to total discharge. Velocity measurements were made at 60% water depth with a Model 2000 Flo-Mate portable flow meter (Marsh-McBirney, Inc). Stream profile data were used to build relationships between stream height and average stream width, depth, and velocity for certain streams. Lake and stream water samples were analyzed for ammonium (NHx), nitrite plus nitrate (NOx), total dissolved N (TDN), total N (TN), total dissolved P (TDP), total P (TP), and dissolved organic carbon (DOC). These samples were filtered (Whatman GF/F, 0.6-0.7 μm) within 24 hours of sampling and frozen until analysis. These analyses were performed with a flow-injected autoanalyzer (Lachat QuickChem 8000). The indophenol blue method (modified from Solorzano 1969) was used for ammonium and the cadmium reaction method (Wood et al. 1967) for NOx and TDN. TDP was measured as soluble reactive phosphorus (Murphy and Riley 1962) after digestion. Dissolved organic nitrogen (DON) was determined by subtracting nitrite, nitrate, and ammonium from TDN (Compton et al. 2003). DOC was measured on an OI Corporation TOC analyzer using a method derived from Menzel and Vaccaro (1964). The method used to analyze TN and TP in 2010 have been altered to enable more accurate assessment of TN and TP in acid-preserved water samples. We are in the process of testing the new TN method for accuracy before using it to analyze the 2011 and 2012 samples for TN. Stream discharge for each stream was calculated using distance, depth and velocity measurements from stream profiles. Discharge values for each stream were multiplied by nutrient concentrations to give nutrient flux data. To infer primary productivity, chlorophyll a (Chl a) from lake samples was analyzed as an indicator of algal photosynthetic activity. Water samples for these analyses were filtered with 0.7-μm polycarbonate membranes (Whatman, USA). Chl a was extracted from the filtrate in a 90% ethanol solution (8-12 hours) and measured fluorometrically (Turner Designs TD-700), following Welschmeyer (1994). In addition, we deployed two funnel traps in each lake to collect sediments through the growing season. These samples were analyzed for biogenic silica (BSi) to estimate the productivity of diatoms, the dominant primary producers of our study lakes. BSi was extracted with 10% Na2CO3 and

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Togiak National Wildlife Refuge – Salmon, alder, and lake productivity, September 2013 U.S. Fish and Wildlife Service determined with a spectrophotometer (Spectronic Genesys5) as described in Mortlock and Froelich (1989). We also collected samples of bacterioplankton and zooplankton abundance to assess potential effects of Alnus-derived nutrients on other trophic levels. A single sample was collected from each lake at a point where water depth was ~18 m. Bacterioplankton samples were fixed with 2% gluteraldehyde in the field and will be stained with DAPI flurochrome in the laboratory. At least 300 bacteria will be counted per sample under epiflouresence microscopy (Porter and Feig 1980). We are in the process of obtaining access to appropriate bacterioplankton sample preparation and epiflouresence microscopy equipment to begin analyzing the bacterioplankton samples. Zooplankton were collected by vertical towing from the Secchi depth to the surface with a plankton net (13 cm diameter, 80 μ mesh). Net content was collected in a 125ml bottle and filtered within 12 hours with Whatman GF/F filter (Whatman USA). The filter was then frozen, freeze-dried in the laboratory, and weighed on an analytical balance (AB54-S Mettler Toledo). Prior to use, each empty filter was washed with distilled water, dried, and weighed. Zooplankton biomass (mg L-1) was calculated as: (Filter mass after collection - Filter mass before collection) x Secchi depth x Net mouth area. To estimate the flux rates (kg ha-1 yr-1) of dissolved nutrients deriving from alder N fixation and associated effects on P, we installed resin lysimeters (Susfalk and Johnson 2002) in soils under alder thickets and under adjacent, non-alder vegetation (but away from the hydrological paths of upslope alder vegetation) in adjacent areas. Our goal here was not to characterize the spatial variability of nutrient dynamics within each watershed, as many more lysimeters would be needed to achieve such a goal. We also recognize that flux rates estimated from the resin lysimeters will not correspond directly with terrestrial nutrient loading to lakes because they do not account for plant uptake and microbial transformations that can occur in hydrological flowpaths. Nonetheless, they will provide process-level insights on changes in N and P availability associated with alder. Specifically, subtracting the nutrients of non-alder lysimeters from those of alder lysimeters should yield the amount that can be attributed to the effects of alder at the sampling location. Lysimeters were collected each following summer and new lysimeters installed. Nitrogen and phosphorus were extracted in 2 M KCl following the methods of Langlois et al. (2003). Extractants will be analyzed colorimetrically for inorganic (ammonium, nitrite plus nitrate) N, and ortho-P using a flow-injected autoanalyzer (Lachat QuickChem)

Alnus cover and other watershed characteristics

This project takes advantage of several existing Togiak Refuge datasets to assess the influence of alder on lake nutrient availability. Within each lake watershed, we summarized land cover, hydrology, soils, and surficial geology using a GIS. The average slope of each watershed will also be computed using a digital elevation model (DEM). In addition to total percent alder cover within each watershed (Table 1), we will estimate alder cover within a 100-m buffer of each lake. To further investigate the influence of Alnus location on lake chemistry, we will calculate a distance-weighted measure of alder

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Togiak National Wildlife Refuge – Salmon, alder, and lake productivity, September 2013 U.S. Fish and Wildlife Service cover (sensu King et al. 2005) by multiplying each alder pixel by the inverse of its distance to the shoreline, summing for a distance-weighted pixel count for the entire watershed, and dividing by the sum of distance-weighted total land in the watershed. Estimation / Analysis We will assess the importance of various independent variables for explaining lake nutrient concentrations and nutrient loading. Spearman’s Rank order correlation analysis will be performed to identify groups of significantly correlated variables. Additionally, we will use partial correlation analysis to evaluate relationships between variables while holding influential variables constant. Devotta (2008) developed a conceptual model illustrating hypothesized nutrient flow from watersheds with Alnus cover to various components of the aquatic foodweb in lakes based on preliminary data from Togiak Refuge (Figure 7). We will test this model using additional data and partial least squares regression (PLS; Carrascal et al. 2009). PLS is well suited for analyzing multivariate data in that it is robust to multicollinearity and low sample sizes (Eriksson et al. 1995). PLS uses variation in the independent and dependent variables to create a model that predicts dependent variables. Additionally, PLS quantifies the influence of each independent variable on predicting dependent variables as a variable influence on projection (VIP) statistic. Wold (1995) indicated that a variable with a VIP of < 0.8 should not be kept in the model. All analyses will be performed using the PLS procedure in SAS (SAS Institute, Inc., Cary, NC, USA, version 9.1.3).

= Aquatic microbial loop

Lake water level

Dissolved P

Phytoplankton

Zooplankton

Dissolved organic C

Dissolved Organic

N

Alder growth in watershed

Bacterio-plankton

+ 0.593*

+ 0.565*

+ 0.664**

+ 0.678**

+ 0.778**

+ 0.623* + 0.547*

+ 0.629**

+ 0.584*

+ 0.649* + 0.697**

Figure 7. Conceptual model for the main interrelationships between alder, nutrients exported from beneath alder and components of the aquatic foodweb. Numbers linking the variables represent significant correlation coefficients. The symbols ‘**’ and ‘*’ indicate significance at P < 0.01 and P < 0.05 respectively. Model from Devota 2008.

14


Results and Discussion Salmon escapement estimates In 2012, aerial salmon surveys were performed on all study lakes except Gechiak Lake on 30-31 August. The Gechiak Lake count was not performed until 12 September 2012. In 2011, we conducted the surveys at nine of the 11 salmon lakes on 25-26 August and completed the counts at Snake and Ualik lakes on 6 September. 2010 counts occurred on 7-8 September. Although counts in 2010 were restricted to study lakes only, in 2011 and 2012, counts also included Upper and West Togiak Lakes in order to apportion the tower count for Togiak Lake (explained below). Surveys were conducted with one observer and pilot in a Robinson R-44 helicopter flying at 10-20 m height above water and at 40-60 kph speed. Weather conditions were excellent throughout the surveys in 2011 and 2012, unlike in 2010, in which high winds and rain caused generally poor visibility. Raw escapement counts showed high variation within lakes over the three years (Table 2, Fig. 8). Summarized counts for all lakes per year demonstrated almost no variation between 2010 and 2011, but the 2012 total was less than half of the preceding years. Table 2. Aerial salmon survey results in 2010-2012. Counts include both live and dead salmon. 2010 2011 2012 Kagati-Pegati 17646 6697 4687 Ohnlik 169 1808 320 Goodnews 2055 7131 5126 Gechiak 1071 768 100 Pungokepuk 426 1330 231 Little Togiak 9612 12845 3170 Snake 27072 21167 2000 Ualik 35345 57220 15837 Nenevok 1600 672 244 Togiak 82800 72409 37952 Togiak-Upper 29263 20610 Togiak-West 1588 1045 Ongivinuk 2760 1942 1547 Total1 180556 183989 71214

1Totals do not include Upper Togiak or West Togiak.

15


0

10,000

20,000

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ak

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giak

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ivin

uk

Raw

cou

nts

2010 2011 2012

Figure 8. Unadjusted aerial counts of sockeye salmon in study lakes on Togiak Refuge, 2010-2012. To correct for underestimation of salmon due to imperfect detectability of fish, we adjusted the raw number of salmon counted to an expanded estimate. The basis for this expansion was derived from additional abundance information available for salmon in four lakes: Togiak, Ualik, Gechiak, and Kagati (collectively referred to here as Correction Lakes). Togiak Lake correction For Togiak Lake, we obtained annual escapement counts from 1960-2012 from a counting tower at the outlet of the lake (Alaska Department of Fish and Game {ADFG} 2002, and unpublished data). For each year of this study, the tower counts were ended while the run was still in progress. Prior ADFG run history was used to calculate the estimated percentage of the run that had not yet occurred, and these percentages (2010: 2.1%, 2011: 2.2%, 2012: 6.1%) were used to expand the tower counts. A portion of the fish counted at the tower pass through Togiak Lake and spawn upstream in West and Upper Togiak Lakes. In order to adjust the estimate to account for the fish passing through Togiak Lake to these lakes, we conducted aerial surveys of West and Upper Togiak Lakes on the same day we surveyed Togiak Lake in 2011 and 2012. We apportioned the tower count for the three lakes using the percentages seen in the aerial

16

Togiak National Wildlife Refuge – Salmon, alder, and lake productivity, September 2013 U.S. Fish and Wildlife Service surveys. We did not survey West and Upper Togiak Lakes in 2010, so we used the average of the 2011-2012 percentages to apportion the 2010 tower count among the three lakes as well. In addition, we calculated annual correction factors by dividing the 2010-2012 aerial counts by the final corrected escapement estimates. Ualik Lake correction The additional population information for Ualik Lake was similar to Togiak Lake, in that the source was an ADFG counting tower located on the Igushik River which operated annually from 1975--2012 (ADFG 2002 and unpublished data). As with the Togiak Lake data, the Igushik River tower counts ended while the runs were in progress, requiring us to expand the estimates using ADFG run history, which necessitated adding 2.2 , 3.4, and 3.4% respectively to the overall tower counts in 2010, 2011, and 2012. The Igushik River tower was located at the outlet of Amanka Lake, which is located downstream from Ualik Lake. Thus, to estimate the number of salmon spawning in Ualik Lake, we subtracted the number of salmon which spawned in Amanka Lake and the intervening river (Kathleen River). To do this, we used aerial survey data for Ualik Lake, Amanka Lake, and Kathleen River which were available from 1954-1991, to calculate an average proportion of the run that spawns in Ualik Lake. We subtracted this average proportion from the total 2010-2012 Igushik River tower counts to estimate total Ualik Lake escapement for the three years. As with the Togiak system, we calculated a correction factor between our 2010-2012 aerial counts and the final corrected estimates from the tower counts to provide a means for adjusting salmon counts in lakes without additional information available. Gechiak Lake correction The data source for Gechiak Lake was a weir operated approximately 10km downstream from the lake in 2010-2012 (personal communication, Teresa Tanner, U.S. Fish and Wildlife Service, Anchorage Fish and Wildlife Field Office), as well as aerial survey data for Gechiak Creek from 1960-2002 (ADFG 2002). Due to high water at times, the weir had breaks in operation during the course of the run. To estimate fish passage during those times, we averaged the run size for three days prior and after the break in time, and used that figure to estimate daily passage. Counting at the Gechiak Lake weir was terminated in 2010 prior to the end of the run. We corrected the estimate by adding seven days, each totaling the average of the final three days counted. Counting continued until the end of the runs in 2011 and 2012, so no addition was made. To determine the proportion of the weir count that spawned in Gechiak Lake, as opposed to remaining in Gechiak Creek, we averaged the aerial survey data for Gechiak Creek, and subtracted the average from the 2010-2012 weir counts of sockeyes. Kagati Lake correction The data source for Kagati Lake was a weir operated annually since 2002 approximately 70 km downstream from the lake on the Kanektok River (personal communication, Tracey Hansen, Alaska Department of Fish and Game Division of Commercial Fisheries,

17

Togiak National Wildlife Refuge – Salmon, alder, and lake productivity, September 2013 U.S. Fish and Wildlife Service Kuskokwim Area). Additionally, aerial surveys of the Kanektok River have been performed by ADFG in most years since 1962. Aerial survey data discriminate salmon in Kagati Lake from those spawning in the river. Both weir counts and aerial counts of the lake were available in 2010 and 2012, but not in 2011. To estimate the number of sockeye salmon which spawned in Kagati Lake, we determined the proportions of fish in the lake and in the river during the ADFG aerial survey in 2010 and 2012. We multiplied these proportions by the final weir counts to estimate escapement to the lake. River aerial data were not available in 2011, so we used aerial survey data from 2003-2010 to calculate an average proportion of sockeyes spawning in the lake vs. the river, and used this to apportion the 2011 weir count. Correction factors for other lakes To develop correction factors (cf) for aerial salmon counts in lakes without additional escapement information (referred to here as Uncorrected Lakes), we averaged the cfs from the Corrected Lakes each year (Fig. 9). The annual averaged cf was divided by the raw aerial count for each lake to estimate total escapement (Table 3, Fig. 10).

0.0

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Perc

ent u

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estim

atio

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Ualik Togiak Gechiak Kagati Average underestimate

Figure 9. Percent underestimation (raw aerial counts divided by corrected escapement estimates) of sockeye salmon counts in study lakes on Togiak Refuge, 2010-2012.

18

Togiak National Wildlife Refuge – Salmon, alder, and lake productivity, September 2013 U.S. Fish and Wildlife Service In 2012, we did not use Gechiak Lake in calculating the average correction, as it may not be representative. The Gechiak Lake aerial survey was performed on September 12th, 12-13 days later than the other lakes in 2012, and later than any lake in all years. Table 3. Aerial counts, correction factors, and estimates of sockeye salmon escapement in study lakes on Togiak Refuge, 2010-2012.

Lake Year Aerial count Correction factor Expanded estimate

Togiak 2010 82800 0.643 128723 Kagati-Pegati 2010 17646 0.111 159484 Gechiak 2010 1071 0.107 10039 Ualik 2010 35345 0.116 304881 Pungokepuk 2010 426 0.244 1745 Ohnlik 2010 169 0.244 692 Goodnews 2010 2055 0.244 8418 Little Togiak 2010 9612 0.244 39373 Snake 2010 27072 0.244 110894 Nenevok 2010 1600 0.244 6554 Ongivinuk 2010 2760 0.244 11306 Total 2010 782110 Togiak 2011 72409 0.529 136788 Kagati-Pegati 2011 6697 0.124 53906 Gechiak 2011 768 0.230 3334 Ualik 2011 57220 0.231 248231 Pungokepuk 2011 1330 0.279 4774 Ohnlik 2011 1808 0.279 6489 Goodnews 2011 7131 0.279 25595 Little Togiak 2011 12845 0.279 46103 Snake 2011 21167 0.279 75973 Nenevok 2011 672 0.279 2412 Ongivinuk 2011 1942 0.279 6970 Total 2011 610575 Togiak 2012 37952 0.276 137283 Kagati-Pegati 2012 4687 0.066 71178 Gechiak 2012 100 0.031 3247 Ualik 2012 15837 0.137 115268 Pungokepuk 2012 231 0.160 1445 Ohnlik 2012 320 0.160 2001 Goodnews 2012 5126 0.160 32058 Little Togiak 2012 3170 0.160 19825 Snake 2012 2000 0.160 12508 Nenevok 2012 244 0.160 1526 Ongivinuk 2012 1547 0.160 9675 Total 2012 406014

19


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Figure 10. Estimated sockeye salmon escapement to study lakes on Togiak Refuge, 2010-2012. Estimated salmon escapement within lakes was strongly correlated across the three years of study (2010 vs. 2011: r = 0.94, p < 0.001, 2010 vs. 2012: r = 0.81, p < 0.003; 2011 vs 2012: r = 0.85, p < 0.001), indicating general coherence in run strength. Estimated escapement in 2010 and 2011 was strongly related to marine derived nutrient levels (Table 1) in lake surface sediments (2010: r = 0.84, p < 0.005; 2011: r = 0.82, p = 0.007), although the relationship was not as pronounced in 2012 (r = 0.64, p = 0.06). Regardless, the data suggest that both the short-term aerial surveys and the longer-term record from the lake surface sediments agree on levels of salmon abundance. Mean alder cover in study lake watersheds (Table 1) was not correlated with estimated salmon escapement in any year (r = -0.14 to 0.12, p = 0.68 to 0.97), nor with salmon density, which was calculated as lake volume divided by estimated escapement (r = -0.22 to 0.28, p = 0.41 to 0.51), nor with marine derived nutrients (r = 0.31, p = 0.34). Thus, any relationships which are detected between lake nutrients and salmon abundance or alder abundance are unlikely to be confounded by autocorrelation between these productivity drivers. Nutrients and productivity Field sampling of lakes and streams occurred as scheduled (Tables 4 and 5) and laboratory analyses of most samples were completed through 2012 (Table 6).

20

Togiak National Wildlife Refuge – Salmon, alder, and lake productivity, September 2013 U.S. Fish and Wildlife Service Table 4. Summary of streams sampled and water level loggers deployed during summer and fall field sampling covering 2010 to 2012.

Streams (summer/fall) Water level loggers Lake 2010 2011 2012 2010 2011 2012

Pungokepuk 1/1 1/1 1/1 1 1 2 Kagati-Pegati 3/3 3/3 3/3 1 1 2

Ohnlik 2/2 2/2 2/2 1 1 2 Goodnews 3/3 3/3 2/2 0 1 2

Gechiak 1/1 1/1 1/1 0 0 0 Little Togiak 3/1 3/1 1/1 0 0 0

Snake 3/3 3/3 3/3 1 1 2 Ualik 4/4 4/4 4/4 0 0 0

Nenevok 2/2 2/2 2/2 0 1 2 Togiak 4/3 4/3 4/4 0 0 0

Ongivinuk 1/1 1/1 1/1 1 1 2 Nagugun 2/1 2/1 1/1 0 0 0

High 1/1 1/1 1/1 0 0 0 Total 30/26 29/28 26/26 5 7 14

Table 5. Summary of lysimeters installed and sediment traps deployed during summer and fall field sampling covering 2010 to 2012.

Lysimeters Sediment traps

deployed/sampled Lake 2010 2011 2012 2010 2011 2012

Pungokepuk 1 1 1 2/0 2/2 2/2 Kagati-Pegati 0 1 1 1/0 2/2 2/2

Ohnlik 0 0 0 0/0 2/2 2/2 Goodnews 0 0 0 2/2 2/2 2/2

Gechiak 0 0 0 2/2 2/2 2/2 Little Togiak 3 3 3 2/1 2/2 2/2

Snake 5 5 5 2/0 2/2 2/2 Ualik 0 0 0 2/0 2/2 2/2

Nenevok 0 0 0 0/0 2/2 2/2 Togiak 0 0 0 0/0 2/2 2/2

Ongivinuk 0 0 0 0/0 2/2 2/2 Nagugun 0 0 0 2/1 2/2 2/2

High 0 1 1 2/1 2/2 2/2 Total 9 11 11 17/7 26/26 26/26

21

Togiak National Wildlife Refuge – Salmon, alder, and lake productivity, September 2013 U.S. Fish and Wildlife Service Table 6. Progress on sample analyses covering 2010 to 2012. Year Field Season Completed Analyses Pending Analyses 2010 Summer (1) Water chemistry: NHx, NOx,

TDN, TDP, TN, TP, DOC, cations*

(2) Lake primary productivity: Chl a, BSi

(3) Lake productivity on other trophic levels: zooplankton

(3) Lake productivity on other trophic levels: Bacterioplankton

Fall (1) Water chemistry: NHx, NOx, TDN, TDP, TN, TP, DOC, cations




2011 Summer (1) Water chemistry: NHx, NOx,

TDN, TDP, DOC, cations (1) Water Chemistry: TN, TP




Fall (1) Water chemistry: NHx, NOx, TDN, TDP, TN, TP, DOC, cations

(1) Water Chemistry: TN, TP




2012 Summer (1) Water chemistry: NHx, NOx,

TDN, DOC (1) Water Chemistry: TDP, cations, TN, TP




Fall (1) Water chemistry: NHx, NOx, TDN, DOC

(1) Water Chemistry: TDP, cations, TN, TP




* NHx: ammonia (NH3) plus ammonium (NH4) NOx: nitrogen dioxide (NO2) and nitrate (NO3) TDN: total dissolved nitrogen TDP: total dissolved phosphorus TN: total nitrogen TP: total phosphorus DOC: dissolved organic carbon BSi: biogenic silica Chl a: chlorophyll A

22

Togiak National Wildlife Refuge – Salmon, alder, and lake productivity, September 2013 U.S. Fish and Wildlife Service Lake and stream water chemistry Spearman Rank correlation analyses on 2010 to 2012 water chemistry data indicates that salmon and alder are related to indicators of aquatic chemistry and productivity (Tables 7 and 8). Percent alder in lake and stream watersheds was consistently related to higher levels of aquatic N (in the forms of NOx, NH4, TDN and DON) and productivity (Chl a) across seasons and years. The timing of alder leaf senescence and litter fall in the Fall season may explain why the relationship between % alder and Chl a was only significant in the fall season of 2011, and not in the summer. Additionally, the negative relationship between NH4 and % alder in 2011 may reflect active uptake of NH4 during the summer period for growth, possibly arising from an unusually long growing season or higher summer temperatures that year. Also, the stream and lake NH4 values associated with % alder cover were lower in summer 2011 than other sampling periods, providing further evidence for this thought. In 2012, Spearman Rank correlation analysis shows that DOC and NH4 concentrations are positively related to watershed alder cover (Table 7). Increased inorganic N (NH4 and NO3

-) concentrations have been found in soils beneath alder as a result of the decomposition of alder-derived N-rich litter and higher nitrification rates (Goldman 1961; Compton et al. 2003). Additionally, organic nutrient additions into riparian areas from alder would also have a large C component, possibly explaining the strong correlation between % alder cover and DOC concentrations. An alternative explanation for this is that alder-derived N may be increasing terrestrial and aquatic productivity as a whole, driving increased terrestrial and aquatic production of DOC. Correlation analyses show that salmon abundance is also positively related to aquatic productivity and nutrient concentrations (Table 8). Chl a concentrations increase with salmon escapement numbers and % MDN, suggesting that lake primary productivity increased with salmon and marine-derived nutrients. Phosphorus (P) concentrations (in the forms of TDP or TP) are consistently strongly associated with salmon escapement numbers and % MDN, indicating greater P availability during the salmon spawning season (Table 8). One exception to this pattern is the negative relationship between TDP concentrations and salmon escapement numbers in Fall 2010. Factors such as stream discharge and rates of biological P uptake impact dissolved P concentrations where salmon spawners are present (Janetski et al. 2009). Thus the negative relation in Fall 2010 may reflect particularly active P uptake. Consistent with this interpretation, the positive relationships between Chl a and zooplankton values with salmon escapement numbers for that season may reflect this rapid P uptake by the biotic community. Correlation analyses were run on all the variables under the ‘completed analyses’ section in Table 5 for each season of each year. Inter-annual variations in temperature, precipitation, and growing season length, along with the sampling time relative to these variations, may explain why certain variables were not consistently related to salmon abundance and alder cover across both seasons and years. Such changes can impact nutrient resource availability by affecting biotic processes such as decomposition, growth and biotic uptake. Moreover, differences in soil temperature and freezing depth also

23

Togiak National Wildlife Refuge – Salmon, alder, and lake productivity, September 2013 U.S. Fish and Wildlife Service impact the structure of sub-surface hydrologic flow paths that carry dissolved nutrients from watershed soils to streams and lakes (Williams et al. 2010). We will refine these interpretations by including temperature, precipitation and growing season length data in the data analyses to better understand the factors accounting for the variation in the relationships among alder, salmon, aquatic nutrient concentrations and aquatic productivity variables. Table 7. Correlation analysis results with % alder cover.

X variable

Season Year Y variable r value p value

2010 TN values 0.66 <0.05 NH4 values -0.59 <0.05 2011 NOx values 0.66 <0.05

NH4 values 0.65 <0.05 NOx values 0.79 <0.01

Summer

2012

TDN values 0.79 <0.01 NOx values 0.77 <0.01 2010 Zooplankton

values 0.53 0.0635

NH4 values 0.69 <0.01 NOx values 0.68 <0.05 TDN values 0.70 <0.01 DON values 0.68 <0.05

2011

Chl a values

0.59 <0.05

NOx values 0.74 <0.01

Lakes % alder in lake

watershed

Fall

2012 TDN values 0.75 <0.01 NOx values 0.63 <0.01 TDN values 0.36 <0.05

2010

TN values 0.45 <0.05 DON values 0.50 <0.01 NH4 values -0.46 <0.05 NOx values 0.73 <0.01

2011

TDN values 0.65 <0.01 NH4 values 0.58 <0.01 NOx values 0.92 <0.01

Summer

2012

DOC values 0.63 <0.01 2010 NOx values 0.81 <0.01

DON values 0.54 <0.01 NOx values 0.81 <0.01

Streams % alder in stream

watershed

Fall 2011

TDN values 0.75 <0.01

24


DOC values 0.41 <0.05 NOx values 0.76 <0.01 TDN values 0.75 <0.01

2012

DOC values 0.54 <0.01

Table 8. Correlation analysis results with salmon abundance indicators.

X variable Season Year Y variable r value p value TDP values -0.66 <0.05 Chl a values 0.62 <0.05

2010

zooplankton values 0.62 <0.05

Salmon escapement numbers**

Fall

2011 TDP values 0.64 <0.05

Fall 2010 TP values 0.64 <0.05

NH4 values 0.55 0.078 TDP values 0.69 <0.05 Fall

2011

Chl a values 0.54 <0.01

Lakes

MDN

Summer 2012 DOC 0.75 <0.01 **Salmon escapement numbers were normalized by dividing salmon escapement numbers for each lake in the Fall season of each year by individual lake depth to account for larger lakes attracting more salmon Lake sediment data Analyses of 2010 to 2012 sediment trap data also reveal patterns of elevated annual lake productivity associated with greater alder cover and salmon. Measurements of sediment BSi, C:N and δ13C come from sediment that was recovered annually from traps in the study lakes. As such, the consistently strong negative relationships among sediment C:N, % alder, salmon escapement numbers and % MDN across both years (Table 9) indicate that annual lake productivity is associated with greater % alder cover, salmon abundance and marine-derived nutrients. Increased aquatic productivity in lakes with greater salmon density is further supported by the consistent negative relationship between sediment δ13C values in 2010 and 2011 with salmon escapement numbers and % MDN (Table 9). Such declines in sediment δ13C values reflect increased algae-derived C to the sedimentary C pool (Hu et al. 2001). Additionally, the lower sediment C:N ratios associated with salmon escapement numbers and % MDN indicate an increased proportion of organic matter that is enriched in N (Hu et al. 2001). No statistically significant correlations exist among mean BSi, % alder cover, salmon escapement numbers or % MDN across years or seasons. This lack of relationship may

25

Togiak National Wildlife Refuge – Salmon, alder, and lake productivity, September 2013 U.S. Fish and Wildlife Service be due to the sediment trap sampling period missing the early spring season, when diatoms typically exhibit highest growth rates (Sommer et al. 1986). Additionally, factors such as water temperature, pH, light depth and lake stratification also influence diatom accumulation rates in lake sediment. Not accounting for these factors may also explain the lack of significant correlations. The lack of statistically significant correlations among sediment C:N, δ13C values, % alder cover, salmon escapement numbers or % MDN in 2012 may have resulted from the unusually long winter experienced in 2012. Changes in the timing of ice-out, length and mean temperature of growing season impact the timing of and duration of lake turnover and subsequent summer stratification. Moreover, small changes in water temperature can interact with nutrient uptake rates in lakes to impact aquatic productivity (Binzer et al. 2012, Kratina et al. 2012). We will incorporate climate data pertaining to the growing season of 2012 into data analyses to investigate these patterns. Table 9. Preliminary correlation analysis results with lake sediment trap data.

Year Y variable X variable r value p value % alder in lake

watershed -0.9 <0.05 sediment C:N


-0.95 0.0513


-0.95 0.0513

2010

sediment δ13C

% MDN -0.87 0.0539 % alder in lake

watershed -0.6 <0.05 2011 sediment C:N

% MDN -0.64 <0.05

(III) Partial Least Squares Regression Analyses on stream N chemistry data We used Partial Least Squares regression analysis (PLS; Carrascal et al. 2009) to evaluate the relative importance of watershed physical characteristics in explaining stream N data from 2010 to 2012. Results reveal that stream N is related to watershed alder cover. Specifically, NOx and TDN (both concentrations and fluxes) are positively correlated with % alder (alder vs. NOx concentration: r = 0.731, p<0.01; alder vs. TDN concentration: r = 0.625, p <0.01; alder vs. NOx flux: r = 0.568, p <0.01; alder vs. TDN flux: r = 0.391, p <0.01). The positive association between % alder cover and stream NOx and TDN can be attributed to the high N inputs from and nitrification rates associated with soils beneath alder. Previous studies evaluating the influence of watershed alder on aquatic chemistry have reported similar patterns (Goldman 1961; Compton et al. 2003; Shaftel et al. 2012). These relationships suggest that % alder cover in the watershed significantly affects N availability in streams.

26

Togiak National Wildlife Refuge – Salmon, alder, and lake productivity, September 2013 U.S. Fish and Wildlife Service PLS analysis yielded significant models for stream N concentrations and fluxes. Three- and one- factor models were obtained for stream N concentrations and fluxes respectively. The number of factors in a model derived by PLS analysis corresponds to the number of orthogonal, underlying, explanatory variables into which the original data set has been decomposed. The R2Y, or PLSR equivalent of the coefficient of determination, for each model was 37.9 and 21.7% respectively. Variable Importance (VIP) values suggest that a combination of alder, physical watershed and temporal variables were key drivers of stream N availability. Alder cover, year, watershed elevation and watershed were among the top-ranked variables for predicting stream TDN, NOx, NH4 concentrations and fluxes (Table 10). The effects of alder on stream N may be constrained by physical watershed characteristics. Strong correlations indicate that lower concentrations of stream N are consistently found at higher watershed elevations and where there are steeper watershed slopes (Table 11). Additionally, greater watershed elevations and slopes are associated with less % alder cover (Table 12). These patterns suggest that physical watershed characteristics have strong control on stream chemistry, by influencing alder distribution and possibly other elemental cycling processes like decomposition and rate of delivery of N to streams. Removing the effects of alder generally weakened the relationships between stream N concentrations and physical watershed characteristics (watershed elevation vs. NOx: r partial = -0.299, p<0.01; watershed slope vs. NOx r partial = -0.14, p=0.227; watershed area vs. NOx: r partial = 0.0209, p=0.849). Similar patterns were found with stream TDN concentrations and NOx flux. These partial correlations give further evidence that combining the influence of alder with physical watershed characteristics is important in explaining variation in stream NOx and TDN concentrations and fluxes. Temporal variables such as year may influence stream N by affecting the duration and rates of weathering and biological activity. Inter-annual differences in precipitation, temperature and growing season length impact alder N fixation and decomposition rates (Mitchell and Ruess 2009). Additional data analyses that examine inter-annual differences in green-up dates, Julian day length and ice-out dates in this region may provide more information on the temporal controls of stream N availability. Table 10. Model coefficients and variable importance values (VIP) from partial least squares regression for predicting N availability in study streams. Only significant models and variables with importance values > 0.8 are shown. Model R2 (%) Predictors VIP Stream N concentration 28.1 Year 1.73 (TDN, NOx and NH4) Watershed elevation 1.73 % alder 1.75 Stream N flux 48.1 Watershed area 1.8 (NOx and NH4) Year 0.9 Watershed elevation 0.8 % alder 0.78

27

Togiak National Wildlife Refuge – Salmon, alder, and lake productivity, September 2013 U.S. Fish and Wildlife Service Table 11. Spearman Rank correlations among stream nutrient concentrations, watershed alder cover and other stream physical watershed characteristics in the study streams (n=113). Only significant correlations (p<0.05) are shown. * p<0.05, **p<0.01

DON (mg L-1) NOx (mg L-1) TDN (mg L-1)

Watershed elevation -0.182* -0.689** -0.612**

Watershed slope -0.221** -0.328** % alder 0.731** 0.625**

Table 12. Spearman Rank correlations among physical watershed characteristics and stream watershed alder cover (n=26). Only significant correlations (p<0.10) are shown.

Watershed area

Watershed elevation

Watershed slope

Watershed elevation 0.4 Watershed slope 0.28 0.664 % alder -0.23 -0.802 -0.272

(IV) Analyses of stream stage, discharge and nutrient fluxes Stream stage and discharge Stream stage records at six lakes covering the summer and fall seasons, from 2010 to 2012, show concordant changes across seasons and years. This suggests that regional rainfall affect most study watersheds similarly. Exceptions to this pattern most likely occurred due to localized events within individual watersheds. An example of such an exception is the negative correlation between stream water levels associated with Kagati-Pegati Lake compared to all the other lakes in 2012 (Table 13). Table 13. Summary of average correlations among water levels from inlet streams in six study lake watersheds from 2010 to 2012.

Goodnews* Kagati-Pegati Nenevok Ohnlik Ongivinuk Pungokepuk

Goodnews*

Kagati-Pegati -0.09

Nenevok 0.93 -0.06 Ohnlik 0.97 -0.06 0.85 Ongivinuk 0.93 -0.07 0.83 0.86

Pungokepuk 0.96 -0.07 0.82 0.94 0.92

Snake 0.88 -0.02 0.81 0.84 0.92 0.92 * Water levels for Goodnews were only obtained from 2011-2012.

28

Togiak National Wildlife Refuge – Salmon, alder, and lake productivity, September 2013 U.S. Fish and Wildlife Service Discharge values from study streams have been calculated from stream profile measurements taken during the summer and fall seasons, from 2010 to 2012. Across the three years, mean stream discharge was higher in the summer than in the fall. Mean stream discharge across both seasons has increased since 2010, reflecting an increasing trend of fall discharge (Fig 11).

Figure 11. Mean stream discharge from 26 study streams where stream profile measurements were taken in the summer and fall, 2010-2012. Stream nutrient fluxes From 2010 to 2012, total dissolved stream N flux has decreased or remained stable across most watersheds (Figure 12). One exception this pattern is the stream in Gechiak lake watershed, where stream N flux has been increasing by about 0.2 mg s-1 m-2 since 2010. This could be due to localized factors such as increased water yield or expanding alder shrub cover in close proximity to the stream in this watershed. Additionally, NOx has been dominating stream N flux across most watersheds over the last two years. The large DON flux from the stream in Little Togiak watershed in 2011 may be an outlier data point that needs to be investigated in more detail. NOx dominating stream N flux may be due to the successively later ice-out dates this region has been experiencing over the last two years. Mineralization of N beneath snowpacks over winter often leads to large pulses of NOx during snowmelt (Williams et al. 2009). As such, as winters last longer and snowpacks remain on the ground later into

29

TogU.S

iak National Wildlife Refuge – Salmon, alder, and lake productivity, September 2013 . Fish and Wildlife Service

30

the summer each year, larger pulses of stream NOx may be indicative of the later timing of snowmelt in the region. Analyses of green-up and spring melt data from the last three years for the Togiak Refuge will be done to confirm this pattern. Across all lake watersheds, average stream DOC flux increases from 2010 to 2011, then decreases in 2012 (Figure 13). Highest DOC fluxes for streams in all lake watersheds occurred in 2011. Changes in stream DOC concentrations may be driving this pattern as average stream DOC concentration follows a similar pattern, increasing from 1.65 to 5.52 mg L-1 from 2010 to 2011, then decreasing to 1.06 mg L-1 in 2012. Variations in annual photoperiod, growing season length and temperature can impact rates of decomposition and photosynthesis in terrestrial and aquatic systems, thereby impacting aquatic DOC concentrations. Data regarding changes in photoperiod and temperatures during the growing season will be obtained for 2010 to 2012 to investigate these patterns. The large DOC fluxes that streams in the High, Kagati-Pegati, Nenevok and Ohnlik lake watersheds carried in 2011 (> 3 mg s-1 m-2) may be due to localized factors. Such factors include large water yields (Collier et al. 1989) or hydrologic flowpaths moving through organic soil, resulting in high DOC concentrations. In 2011, average stream discharge from High, Kagati-Pegati and Nenevok lake watersheds were among the five largest stream discharge values (> 1350 L s-1) averaged by lake watershed and season among the 13 lake watersheds. Large water yields promote greater flushing through the soil and increasing DOC with increasing discharge (Carey 2003). Indeed, average stream DOC concentrations from High, Kagati-Pegati, Nenevok and Ohnlik lake watersheds were greater than 6 mg L-1 in summer 2011. Elevated stream DOC concentrations may have also resulted from hydrologic flowpaths moving through near-surface organic soil layers during snowmelt (Carey 2003) in these four lake watersheds. Similar to the patterns in stream DOC flux, stream TDP flux increases from 2010 to 2011 across most lake watersheds (Figure 14). As average stream TDP concentration decreases from 4.24 ug L-1 to 3.34 ug L-1 over this period, higher mean stream discharge in 2011 compared to 2010 (Figure 12) may account for this pattern. Greater amounts of soluble P resulting from desorption, dissolution and extraction of P from soil and organic material are mobilized under larger surface and sub-surface flow (Ontkean et al. 2005). One exception to this pattern is Ualik lake watershed, where stream TDP flux decreases from 2010 to 2011. As mean stream discharge from the Ualik watershed increases over this period from 242 L s-1 to 472 L s-1, decreased stream TDP flux from this watershed is likely due to reduced TDP concentrations. Decreased rates of weathering of mineral substrate or mineralization of soluble P from organic material may account for reduced TDP concentrations in this watershed. 2012 TDP nutrient fluxes will be calculated once TDP analyses of the water samples are completed.


Stream

N flux averaged by lake watershed

and

season (m

g s‐1 m

‐2)

Figure 12. Stream N flux averaged by lake watershed and season, 2010-2012.

31


Stream

DOC flu

x averaged

by lake watershed

and

season (m

g s‐1 m

‐2)

Figure 13. Stream DOC flux averaged by lake watershed and season, 2010-2012.

32

TogU.S

iak National Wildlife Refuge – Salmon, alder, and lake productivity, September 2013 . Fish and Wildlife Service

33

Figure 14. Stream TDP flux averaged by lake watershed and season, 2010-2011* * 2012 TDP nutrient fluxes will be calculated once TDP analyses of the water samples are completed.

Stream

TDP flu

x averaged

by lake watershed

and

season (m

g s‐1 m

‐2)

Togiak National Wildlife Refuge – Salmon, alder, and lake productivity, September 2013 U.S. Fish and Wildlife Service Soil nutrient fluxes 2011 We measured soil nitrogen and phosphorus fluxes using resin lysimeters deployed from July 2010 until June 2011. All lysimeters were placed below the organic horizon, in the mineral soil. Inorganic nitrogen and phosphorus leaching from the mineral soil was consistently higher under alder-dominated patches than non-alder patches (Table 14). Averaged across watersheds, alder patches leached 27.2 kg ha-1 yr-1 (±12.2 SE) and non-alder patches leached 1.12 kg ha-1 yr-1 (± 0.42) of NOx (NO2 + NO3) (habitat: F=29.2, P=0.001). There was no significant difference in NOx leaching among watersheds. Similarly, alder-dominated patches leached 1.64 kg ha-1 yr-1 (± 0.66) and non-alder patches leached 0.64 (± 0.35) kg ha-1 yr-1of NH4; however, neither these differences nor differences in leaching among watersheds were significant (F=2.85, P=0.13). There was a strong trend for higher dissolved reactive phosphorus (DRP) leaching under alder (mean: 0.79 ± 0.29 kg ha-1 yr-1) than non-alder patches (mean: 0.33 ± 0.17 kg ha-1 yr-1) (F=4.70, P=0.06), whereas watershed had no effect on DRP leaching. Table 14. Mean soil nutrient flux data (± 1 SE) calculated for each lake watershed and habitat sampled, 2010-2011. Lake Habitat NH4

kg ha-1NOxkg ha-1

DRP kg ha-1

Little Togiak Non-alder 1.49 (0.93) 0.90 (0.23) 0.81 (0.37) Little Togiak Alder 0.93 (0.42) 23.24 (6.30) 1.10 (0.75) Pungokepuk Non-alder 0.15 (0.02) 0.05 (0.01) 0.13 (0.11) Snake Non-alder 0.22 (0.03) 1.61 (0.75) 0.09 (0.02) Snake Alder 1.93 (1.03) 25.20 (14.02) 0.64 (0.31) 2012 Lysimeters were deployed from June 2011 until July 2012. Unlike in 2010, lysimeters were placed in two positions: just below the litter layer to measure nutrient leaching from litter (hereafter “surface”); and below the organic horizon, in the mineral soil, to measure subsurface leaching (hereafter “subsurface”). Surface fluxes of NH4 were significantly lower in alder-dominated patches than non-alder patches in 2012 (Table 15). Averaged across watersheds, surface leaching was 2.47 kg NH4-N ha-1 yr-1 (± 1.01 SE) in alder patches and 9.91 kg NH4-N ha-1 yr-1 (± 3.23) in non-alder patches (F=15.6, P=0.003). There was no effect of watershed on surface leaching of NH4 (F=2.91, P=0.10). In contrast, surface leaching of NOx (NO2 + NO3) was higher in alder patches than non-alder patches, with 49.9 kg NOx-N ha-1 yr-1 (± 14.9) leached from alder and 15.3 kg NOx-N ha-1 yr-1 (± 10.1) leached from the surface of non-alder patches (F=10.7, P=0.008). Surface NOx leaching was significantly higher in the Kagati-Pegati Lake watershed than in the High Lake watershed (Tukey’s HSD: P=0.037; Table 15). Subsurface leaching of NH4 did not differ with vegetation type, consistent with the pattern observed in 2011. Averaged across watersheds, subsurface leaching was 0.90 kg NH4-N ha-1 yr-1 (±0.40) under alder-dominated patches and 1.20 kg NH4-N ha-1 yr-1 (± 0.40) under non-alder patches (F=0.25, P=0.63). There was substantial variation in subsurface leaching of NH4 among

34

Togiak National Wildlife Refuge – Salmon, alder, and lake productivity, September 2013 U.S. Fish and Wildlife Service watersheds (F=3.73, P=0.03) driven by significantly higher subsurface NH4 leaching in the Little Togiak Lake watershed than in the Snake Lake watershed (Tukey’s HSD: P=0.05; Table 15). Although annual subsurface NOx leaching was lower in 2012 than in 2011, vegetation had similar effects on leaching in each year. On average, alder-dominated and non-alder patches leached 35.6 kg NOx-N ha-1 yr-1 (± 12.2) and 2.9 kg NOx-N ha-1 yr-1 (± 0.98), respectively (F=14.8, P=0.001). Subsurface NOx leaching did not vary with watershed (F=0.34, P=0.80). We observed higher surface leaching of DRP in alder-dominated than non-alder patches in two of the three watersheds in which we measured surface DRP flux (Table 15). Averaged across watersheds, surface leaching of DRP was 5.59 kg PO4-P ha-1 yr-1 (± 2.51 SE) in alder-dominated patches and 2.93 kg PO4-P ha-1 yr-1 (± 1.18) in non-alder patches. These differences were not explained by vegetation (F=0.61, P=0.45). Rather, there was evidence that watersheds varied considerably in surface DRP leaching (F=3.11, P=0.10) and that vegetation effects depended on watershed (F=2.15, P=0.18). In contrast to 2011, we found no evidence that subsurface leaching of DRP was higher under alder-dominated than non-alder patches. Mean subsurface DRP leaching was 0.73 (± 0.33 SE) and 0.95 (± 0.41) kg PO4-P ha-1 yr-1 under alder and non-alder patches, respectively (F=0.01, P=0.93). Watersheds varied with respect to subsurface leaching (F=2.76, P=0.08), with Little Togiak Lake watershed showing higher leaching rates than the other watersheds (F-Protected LSD: P<0.07 for all comparisons; Table 15). Table 15. Mean (± 1 SE) nutrient fluxes from litter (surface) and mineral (subsurface) soils for by lake watershed and vegetation type, 2011-2012. Lake Watershed Position Vegetation NH4-N

(kg ha-1 yr-1) NOx-N (kg ha-1 yr-1)

DRP (kg P ha-1 yr-1)

High Surface alder 3.04 (2.34) 29.1 (13.34) 2.57 (1.66) High Surface non-alder 8.68 (1.19) 1.42 (0.93) 2.80 (0.42) KP Surface alder 3.86 119.4 20.0 KP Surface non-alder 28.5 72.9 9.46 Snake Surface alder 1.44 (0.77) 47.5 (19.12) 3.80 (0.73) Snake Surface non-alder 4.94 (1.00) 10.0 (7.38) 0.87 (0.52) High subsurface alder 0.87 (0.73) 12.1 (7.26) 0.35 (0.29) High subsurface non-alder 0.32 (0.03) 1.46 (0.38) 0.22 (0.07) KP subsurface alder 3.46 86.2 0.07 KP subsurface non-alder 0.60 (0.01) 0.88 (0.52) 0.22 (0.13) Little Togiak subsurface alder 0.73 (0.02) 74.7 (23.43) 2.11 (1.10) Little Togiak subsurface non-alder 2.70 (0.80) 2.72 (1.24) 2.31 (0.93) Snake subsurface alder 0.19 (0.03) 16.1 (12.56) 0.42 (0.15) Snake subsurface non-alder 0.47 (0.19) 5.98 (3.26) 0.36 (0.19)

35

Togiak National Wildlife Refuge – Salmon, alder, and lake productivity, September 2013 U.S. Fish and Wildlife Service Alnus cover and other watershed characteristics Alder cover estimates from the Togiak Refuge landcover map (Collins 2003) were refined by using a multi-date approach to classifying landcover in Erdas Imagine. In addition to the original Landsat 5 image from August 1989 used in the existing landcover classification, a Landsat 7 layer from June 2009 was included. Because of differences in color and timing of fall senescence for willow and alder, more unique signatures were achieved through this method. Individual bands in each layer were radiometrically corrected, and NDVI layers of each date were also created and included. A supervised classification was performed using data from the training sites of the original Togiak Refuge landcover study (Collins 2003). Revised values for percent alder cover in each lake watershed are listed in Table 1.

An accuracy assessment was performed on the classified image in fall 2011 to evaluate the classification accuracy (Table 16). We used data from test areas sampled in June 2003 to create an error matrix and determine producer and user accuracy for the alder class. We found that the revised map had high degrees of producer and user accuracy for the alder class, 100% and 92.8%, respectively. However, we interpreted these findings cautiously because only a small number of test areas (n = 30) were available and the study area is large.

Table 16. Error matrix resulting from classifying original training set pixels.

Training set data (known cover types)

Alder Willow Other Total Alder 13 1 0 14

Classification Willow 0 10 1 11data Other 0 5 0 5

Total 13 16 1 30 Thus, we performed a second, and more comprehensive accuracy assessment in 2012. Training data were provided by Tony Demarco, USDA Natural Resource Conservation Service, which was under contract in 2011 and 2012 with Togiak Refuge to perform a soil survey of an area which includes the lake productivity study area. The assessment included 161 training sites, 30 of which were alder sites (Table 17). Using these training data, producer and user accuracy was 70% and 47% (respectively) for classifying alder stands correctly at all training sites. Accuracy increased to 74% and 93% for training sites of large, homogeneous landcover stands (n = 56). When compared with the accuracy level of the original land cover map (47% and 54% producer and user accuracy), the revised map is significantly more accurate. Revised values for percent alder cover in each lake watershed are listed in Table 1.

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Togiak National Wildlife Refuge – Salmon, alder, and lake productivity, September 2013 U.S. Fish and Wildlife Service Table 17. Error matrix resulting from classifying NRCS training set pixels.

Training set data (known cover types)

Alder Willow Other Total Alder 21 9 15 45

Classification Willow 4 9 30 43data Other 5 13 55 73

Total 30 31 100 161 Acknowledgements This project was funded by the U.S. Fish and Wildlife Service and the University of Illinois. We thank pilots R. Grant, T. Schlagel, M. Rousseau, and M. Hink for their safe flying. Thanks for field assistance from A. Aderman, S. Piazza, and M. Winfree, and we’re grateful to H. Lauren for assistance with design, field work, and analysis during the initial year of the project.

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