DEPARTMENT OF THE INTERIOR
U.S. FISH AND WILDLIFE SERVICE
REGION 7
FY14 ENVIRONMENTAL CONTAMINANTS PROGRAM
ON-REFUGE (OR OFF-REFUGE) INVESTIGATIONS SUB-ACTIVITY
AK - Effects of roads and climate on water quality and temperature in lentic wetlands:
Do roads facilitate amphibian malformations through poor water quality and thermal
disturbance?
Project ID: FFS#7N26 and DEC ID#201070001.3
(filename: 7N26_Roads_AmphibianAbnormalities_ClimateChange_Final2014.pdf)
by
Mari Reeves and Margaret Perdue
Environmental Contaminants Specialists
for
Socheata Lor, Field Office Supervisor
Anchorage Fish and Wildlife Field Office
Anchorage, AK
September 30, 2014
Congressional District AK #109
TABLE OF CONTENTS
LIST OF FIGURES ............................................................................................................................................ 4
LIST OF DATA FILES........................................................................................................................................ 5
EXECUTIVE SUMMARY .................................................................................................................................. 7
II. PROPOSAL ABSTRACT ............................................................................................................................... 8
KEYWORDS ................................................................................................................................................ 8
II. INTRODUCTION ......................................................................................................................................... 9
Background: .............................................................................................................................................. 9
III. METHODS AND MATERIALS ................................................................................................................... 13
Site Selection........................................................................................................................................... 13
Field methods ......................................................................................................................................... 18
Biweekly Field Visits and Amphibian Monitoring ............................................................................... 18
Metamorphs and Abnormality Assessments ...................................................................................... 18
Water Quality and Contaminants Sampling Summary ....................................................................... 19
Water Quality and Quantity Measurements ...................................................................................... 21
Water Sampling................................................................................................................................... 22
Contaminants Sampling in other Media ............................................................................................. 24
QA/QC for sampling ............................................................................................................................ 26
Data-Logger Measurements ............................................................................................................... 26
Habitat Assessments ........................................................................................................................... 27
Invertebrate Community Assessments ............................................................................................... 28
IV. RESULTS ................................................................................................................................................. 28
Description of Data ................................................................................................................................. 28
Kenai Data (Historic Studies)............................................................................................................... 28
Kenai Data (Present Study) ................................................................................................................. 29
Anchorage Data (2009 Project) ........................................................................................................... 31
Site Locations .......................................................................................................................................... 33
Site Monitoring ....................................................................................................................................... 34
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Amphibian Data ...................................................................................................................................... 34
Breeding and Metamorphosis Dates .................................................................................................. 34
Frog Development .............................................................................................................................. 35
Frog Abnormalities and Disease ......................................................................................................... 36
Water Quality Data ................................................................................................................................. 39
Analytical Data ........................................................................................................................................ 40
Analytic Attributes .............................................................................................................................. 40
Analytic Results ................................................................................................................................... 41
Temperature logger data ........................................................................................................................ 43
Conductivity Logger Data ........................................................................................................................ 44
Road data ................................................................................................................................................ 46
Habitat .................................................................................................................................................... 46
Wetland Depth and Volume ................................................................................................................... 49
Invertebrate Community Data ................................................................................................................ 50
Disease Data............................................................................................................................................ 51
Batrachochytrium dendrobatidis (Bd) ............................................................................................... 51
Bd in Water ......................................................................................................................................... 51
Water Quality Measurements Concurrent with Bd in Water Sampling ............................................. 52
Perkinsus Like Organism ..................................................................................................................... 53
Anchorage Study ..................................................................................................................................... 53
Descriptions of the Anchorage Water Quality Study and Quality Control Information ..................... 53
Anchorage Sample Log........................................................................................................................ 55
Sediment Data..................................................................................................................................... 56
Water Data .......................................................................................................................................... 57
Summary of Controlled Experiments ...................................................................................................... 59
Controlled Experiment 1:..................................................................................................................... 59
Controlled Experiment 2:..................................................................................................................... 60
V. DISCUSSION ............................................................................................................................................ 61
3
Paper 1: Effect of roads and climate on water quality, habitat, and wetland community composition
across an urbanization gradient in South-central Alaska ....................................................................... 61
Paper 2: Do contaminants, climate change, and habitat characteristics influence amphibian
abnormalities in the wild? ...................................................................................................................... 61
VI. MANAGEMENT RECOMMENDATIONS .................................................................................................. 62
VII. REFERENCES .......................................................................................................................................... 64
VIII. FY2014 REVIEW AND APPROVAL ......................................................................................................... 70
LIST OF FIGURES
Figure 1.Percentages of skeletal and eye abnormalities found in ten year survey of National Wildlife
Refuges of the USA. Figure from Reeves et al. (2013) DOI: 10.1371/journal.pone.0077467. ................... 10
Figure 2. Map of Alaska, Kenai Refuge Boundaries, and study area delineated by Sector Boundaries in
red. .............................................................................................................................................................. 14
Figure 3. Study Area with sector boundaries and streets and main roads used for site selection and 1 km
buffer around them highlighted green. ...................................................................................................... 16
Figure 4. Map of Kenai Study Sites. ............................................................................................................ 20
Figure 5. Schematic of locations of media sampled for metals with respect to road and wetland. .......... 24
Figure 6. Locations of current and historic study sites in Kenai with relationships to roads and traffic.
AADT=Average annual daily traffic data from the Alaska State Department of Transportation. Available
at: http://www.dot.state.ak.us/stwdplng/mapping/adt.shtml.................................................................. 29
Figure 7. Map of current Kenai study sites with Anchorage sites and road traffic data. AADT=Average
annual daily traffic data from the Alaska State Department of Transportation. Road data available at:
http://www.dot.state.ak.us/stwdplng/mapping/adt.shtml ....................................................................... 32
Figure 8. Map of Anchorage roads and wetlands water quality study sites............................................... 33
Figure 9. Patterns in Temperature Data. Top panel shows box and whisker plots of temperature data at
deep (D) and shallow (S) loggers at each site. Bottom three panels show variation in temperature data
within and across sites. Blue series are deep loggers and green series are shallow loggers; 2011 and 2012
data are presented for each site to show variation with time as well as with depth. Panels suggest
different amounts of mixing between deep and shallow waters within sites, suggesting different
hydrology. ................................................................................................................................................... 45
4
LIST OF DATA FILES
AnalyticAttributes.csv. File contains notes taken during collection of analytic chemistry samples,
location data for transect samples, and other information that may assist with analysis of the analytical
chemistry data.
Analytic Results.csv. This dataset includes the laboratory analytical information for all samples collected
for analysis of metals, organic carbon, and anions as described in the "Methods.txt" file. For this project,
we collected snow, soil, dust, wetland sediment, water, periphyton, and tadpole tissue.
ANC_WQ_Study_2009.xlsx. This excel file contains summary information from a pilot-scale study that
was conducted in the fall of 2009 to inform the sampling design of the Kenai study that is the focus of
this data submission.
AnchorageSampleLog.csv. This file contains information about sites sampled for the Anchorage Water
Quality Pilot Study and a join field to the “SiteLocations.csv” file, which provides the latitude and
longitude for all sites included in this data submission.
AnchorageSedimentMetals.csv. This file provides metals concentrations in sediment for the Anchorage
2009 study. These are sediment digest samples analyzed by ICP/MS for metals, and are comparable to
the sediment samples from the Kenai data set, based on the methods used.
AnchorageWaterMetals.csv. File provides metals concentrations in water for the Anchorage 2009 study.
These are metals data from unfiltered water samples, and are comparable to the samples labeled as
sample type, “total” from the Kenai data set, based on the methods used.
BDFilterResults.csv. Data set includes information on 222 water filters that were analyzed for Bd
(Batrachochytrium dendrobatidis), a fungal pathogen that can cause chytridiomycosis in amphibians.
BreedingMetamorphosisDates.csv. Data in this file describe the developmental period of Lithobates
sylvaticus tadpoles in study sites from 2010 through 2012.
Conductivity.csv. File contains data from 6 continuous conductivity loggers deployed at 6 study sites.
DataSummaryReport.pdf. This file provides study background and repeats information in the
SiteSelection.txt and Methods.txt documents with maps and other figures as well as describes each data
file in this submission.
FrogAbnormalities.csv. This data set includes information on abnormalities in each of the 9,011
individual amphibians surveyed on the Kenai Refuge between 2000 and 2012.
5
FrogDevelopment.csv. This table describes each site visit from 2010–2012, whether frogs were found
and what developmental stage they were, and how much time was spent assessing the site and
performing the frog survey.
Habitat.csv. Data set includes habitat data, including vegetative, structural, and sediment characteristics
of the site and the surrounding area.
Invert.csv. Data set includes information on invertebrates. Effort was made to survey the entire
invertebrate community, not just predators of larval amphibians.
Kenai_Roads_AmphibianAbnormalities_ClimateChange_Proposal.pdf This file describes the original
background, goals, and objectives for the study.
Methods.txt. This file describes field methods in a .txt format to ensure readability in multiple computer
formats. Text in this document is repeated with maps and other figures in the
“DataSummaryReport.pdf” file included with this data submission.
RoadsInfo.csv. File provides measures of distance from each wetland site to the nearest road and
whether that road was paved or gravel.
SiteSelection.txt. Description of methods for choosing study sites by stratified random design. This file
describes site selection in a .txt file to ensure readability in multiple computer formats.
SiteEvents.csv. Data set includes monitoring data collected approximately every 14 days during the
summer months.
SiteLocations.csv. File contains location information for all wetland sites in Kenai and Anchorage.
Limited water quality and metals data are available for sites in Anchorage. Some sites in Kenai only have
location and frog abnormality data, and link to the “abnormality.csv” dataset.
Temperature.csv. File contains information from continuous temperature loggers deployed at each site
for the summer months during 2010-2012.
WaterQuality.csv. This table describes water quality information collected with a handheld water
quality meter between 2010 and 2012.
WaterQualityBDFilters.csv. This table describes water quality information collected with a handheld
water quality meter concurrent with and spatially collocated with Batrachochytrium dendrobatidis
samples in water.
WetlandDepthVolume.csv. Data set includes wetland depth, area, and volume.
6
EXECUTIVE SUMMARY
We conducted a three-year field study on the Kenai National Wildlife Refuge in Southcentral
Alaska to investigate how road runoff and climate change influenced abnormalities found in
local wood frog (Lithobates sylvaticus) populations since 2000. This study was designed to test
ecological mechanisms proposed and developed in four recent publications (Reeves et al. 2008,
2010, 2011 and 2013). The goals of the study fell into two broad categories: (1) to describe the
fate and transport of metal contaminants from roads to adjacent habitats and 2. to test
ecological mechanisms whereby elevated metal concentrations facilitated higher than expected
abnormality frequencies in frogs through interactions among toxicants, elevated temperatures,
and predators. Site selection followed a stratified random design, in which potential wetlands
(defined as within 1 km of roads on the Kenai Peninsula) were stratified based on road type
(paved versus gravel) and chosen to span variable traffic levels and distance from the road.
Thirty-six study sites were chosen. Under objective 1, we collected samples of snow, soil, and
dust that may be contaminated by road and vehicle wear at transect points 5m and 50m from
the road shoulder. These samples were replicated at the same locations in 2011 and 2012,
providing an assessment of interannual variability. For objective 2, we collected a large suite of
information about wetland size, volume, habitat, local rainfall, amphibians (timing of breeding,
development, metamorphosis and abnormalities), and the invertebrate community. Perhaps
most significantly, we designed the study to assess spatial and temporal variability in
temperature, basic water quality measures (acidity, dissolved oxygen, salinity, conductivity, and
turbidity), and the concentrations of metals, organic carbon, and anions. Each site was
instrumented with continuous data loggers for temperature at deep and shallow locations and
was sampled repeatedly during spring and summer months for metals and water quality.
Incidental to these main objectives, we also collected information on the disease
Batrachochytrium dendrobatidis (Bd) in water and a much more limited dataset on metals
concentrations in water and sediment data for similar habitat types in Anchorage. Two journal
publications have already resulted directly from this study (Reeves et al. 2011 and Rinella et al.
2012) with one more in the final stages of preparation (Hayden et al. in prep). Nevertheless the
bulk of the field data has not been analyzed or included in a peer reviewed publication yet due
to cuts to the federal budget during the study term. We therefore are sharing this information
online in hopes that it will facilitate analyses useful to the public or land managers in the future.
7
II. PROPOSAL ABSTRACT
Gross physical abnormalities have been documented in Kenai National Wildlife Refuge (NWR)
wood frogs since 2000. Abnormality frequencies in South central Alaska are elevated relative to
other areas of the continental US, making south-central Alaska a significant “hotspot cluster”
that includes a large number of sites with abnormal frogs. Toxic metals and invertebrate
predators were correlated with the abnormalities in a prior analysis, as was proximity of the
breeding site to the nearest road. Roads may contribute metals to wetland water and sediment,
including those toxic metals found to be positively correlated with the abnormalities in Kenai
(Copper, Iron, Nickel, and Zinc). Moreover, Alaska is experiencing climate change more rapidly
and extensively than lower-latitude regions. Wetland habitat on the Kenai NWR is experiencing
progressive drying attributed to climate change. This project aims to examine the multiple
stressor effects of road-based metal contamination and warmer, drier conditions in Kenai NWR
wetlands, and how these factors may contribute to the high incidence of amphibian
malformations in this model system.
KEYWORDS
FFS#7N26, DEC ID#201070001.3, congressional district AK #109, Kenai National Wildlife Refuge,
Alaska, Anchorage, amphibian, water quality, road, temperature, climate, metal, anion, DOC,
invertebrate, wetland, abnormality, malformation, disease, frog, lentic, Lithobates sylvaticus.
8
II. INTRODUCTION
Background:
Amphibian abnormalities are probably caused by interactions among multiple stressors in most
cases. For example, Johnson et al. (2007) found that nutrient pollution increased parasiteinduced limb abnormalities by increasing primary productivity, which allowed the parasite’s
snail host to become more abundant and increased the success of parasite reproduction.
Kiesecker (2002) found that chemical contamination decreased tadpole immune response
allowing for greater infection by this same parasite. Although parasite-induced abnormalities
are becoming better-understood, in areas like southcentral Alaska, where parasites are not
implicated, there remains no universally-accepted mechanism for the most commonly observed
amphibian abnormalities—shrunken and missing limbs and limb elements, (Figure 1; Linzey et
al. 2003; Taylor et al. 2005; Bacon et al. 2006; Skelly et al. 2007; Reeves et al. 2008). Correlative
studies have repeatedly linked amphibian abnormalities to chemically or physically altered
habitat (Ouellet et al. 1997; Hopkins et al. 2000; Linzey et al. 2003; Taylor et al. 2005; Bacon et
al. 2006; Gurushankara et al. 2007; Reeves et al. 2008), yet manipulative experiments that
provide causative evidence for non-parasite induced amphibian abnormalities are lacking.
Other than parasites, the best experimentally-supported causes of amphibian abnormalities are
chemical contaminants and predators (reviewed in Johnson et al. 2010). Yet, as people design
increasingly more complex experiments, it is becoming clear that interactions between abiotic
and biotic factors (such as contaminants and predators) may cause abnormalities in disturbed
areas.
Negative environmental effects of roads and vehicles are well established (Driscoll et al. 1990;
Buckler and Granato 1999; Eriksson et al. 2007). Deleterious effects of contaminants in road
runoff on water quality and biota in receiving waters are also clear (Maltby et al. 1995; Boxall
and Maltby 1997; Breault and Granato 2000; Trombulak and Frissell 2000). These effects
include contamination of water and sediments with heavy metals and uptake of these metals
by plants, insects, and animals including amphibians (Sriyaraj and Shutes 2001; Scher and Theiry
2005; Pratt and Lottermoser 2006). In these studies, the presence of metals in the biota
resulted in poor fitness and compromised reproductive success in animals, and decreased
biological diversity and productivity in the wetlands they inhabited.
9
Figure 1.Percentages of skeletal and eye abnormalities found in ten year survey of National
Wildlife Refuges of the USA. Figure from Reeves et al. (2013) DOI:
10.1371/journal.pone.0077467.
10
The mechanisms of contaminant transport may vary by road type (Claiborn et al.1995;
Sutherland and Tolosa 2001; Memon et al. 2005). For example, on paved roads, runoff of water
pooled on the impervious surface may be an important transport mechanism during spring
thaw or summer storms (Sansalone and Buchberger 1997; Vaze and Chiew 2002; Nie et al.
2008). On gravel roads, metals may enter adjacent wetlands bound to dust from the roadbed or
road shoulder, or water percolation through the roadbed itself may contribute to groundwater
contamination (Claiborn et al. 1995; Edvardsson and Magnusson 2009). Although it might be
hypothesized that road effects are only deleterious at high traffic volumes and in urban areas,
we designed this study to examine the relationship between contaminant concentrations and
traffic volume more closely (Drapper et al.2000; Patel 2005). Several prior studies have shown
ecologically and biologically significant effects of contaminants even at low traffic levels
(Baekken 1994; Cooper et al. 1996; VanDolah et al. 2005).
It may also be hypothesized that more roads lead to worse outcomes for adjacent water
bodies. Wetlands in urban areas may have degraded water quality characteristics such as
higher temperatures, higher pH, greater algal biomass, higher alkalinity and conductivity, and
lower dissolved oxygen, altered hydrology, and higher nutrient loads (Karouna-Reiner and
Sparling 2001; Decatanzaro et al. 2009; Decatanzaro and Chow-Fraser 2010). Impervious
surfaces may increase contaminants in runoff and toxicity to biota in receiving waters (Driscoll
et al. 1990; Maltby et al. 1995; Boxall and Maltby 1997; Buckler and Granato 1999; Breault and
Granato 2000; Trombulak and Frissell 2000; Eriksson et al. 2007). But no study has yet
examined the relationship between urbanization and wetland water quality in south-central
Alaska.
In Alaska, where snow comprises a significant portion of annual precipitation, snow melt may
contribute a pulse of contaminants during spring, causing high pollutant concentrations in
runoff (Dupuis et al. 1985; Backstrom et al. 2003; Westerlund and Viklander 2006). In spring,
there may also be a pulse of acidity into freshwater systems because snow lacks buffering
capacity that comes through soil contact in some places (Sadinski and Dunson 1992; Campbell
et al. 1995; Vertucci and Corn 1996). These acidic conditions may increase metal dissolution
from impervious surfaces and mobilize dissolved metals into wetlands (Beattie and Tyler-Jones
1992; Wyman and Jancola 1992). Use of gravel and deicers on roads create additional sources
of contaminants (Hussein et al. 2008). The use of traction sand and studded tires on the road
surface cause even greater road wear, thus contributing particulate toxicants by grinding both
car tires and the road surface itself. Furthermore, both traction sand and tires can contain Zn
and other metals (Kupiainen et al. 2003; Blok 2005; Norman and Johansson 2006).
11
Habitat is a key component of ecosystem dynamics and can affect the interpretation of water
quality data. Habitat type and hydrology can affect water quality by influencing contaminant
concentrations or toxicities (Cabezas et al. 2009). For example, deeper wetlands or those
through which water flows faster may be more resistant to changes in pollutant concentrations
simply because of dilution. Conversely, wetlands with a dense vegetative community in the
water column may facilitate higher levels of organic carbon, known to protect aquatic
organisms from metals toxicity (Gheorghiu et al. 2010). By measuring habitat-based cofactors,
we designed this study to learn how habitat features make wetlands more sensitive (or
resistant) to changes caused by urbanization, with the management outcome of targeting
different habitat types for future storm water management interventions.
Finally, Alaska is experiencing climate change more rapidly and extensively than lower-latitude
regions (IPCC 1997; ACIA 2005; Anisimov et al. 2007). This differential warming of the north has
allowed researchers to document negative effects of climate change in Alaska (Berg et al.
2006), including drying of the terrestrial wetlands that serve as wood frog breeding habitat
within Kenai NWR boundaries (Klein et al. 2005; Berg et al. 2009). In addition to the more
general effects of climate change on temperature, roads may act as additional sources of heat,
ones that could be exacerbated by climate change due to the albedo effect on darker paved
surfaces. In studies examining the thermal characteristics of road runoff in high latitude regions,
temperatures measured for asphalt ranged between 36-45°C in locations in Canada, Sweden
and Wisconsin, USA, and runoff produced from these surfaces ranged from 26-36°C (Thompson
et al. 2008). Average temperatures for wood frog breeding ponds in the Kenai NWR ranged
from 6-23°C, lower than the temperatures for initial runoff, which is therefore potentially
capable of having a deleterious effect. A study by Thomann and Mueller (1987) found Chinook
salmon acclimatized to water at 24°C experienced a 50% mortality rate within 24 hours of
water temperature being increased by 2°C. Increased temperatures of road runoff may also
increase metals dissolution in that runoff thus heightening their potential bioavailability (Davies
1986, Heugens et al. 2001).
Here we present information gathered to examine the mechanisms of metal-based
contamination from roads. We explore how different road characteristics like paving, traffic, or
distance from the habitat of interest influence that relationship. We describe how this road
based metal contamination may alter ecological community dynamics in adjacent aquatic
habitats.
12
III. METHODS AND MATERIALS
Site Selection
The Kenai National Wildlife Refuge (KNWR) comprises 797,200 ha in south-central Alaska,
including four designated wilderness areas: Mystery Hills, Swanson River, Skilak Lake, and
Tustemena Lake (Wilderness Act of 1964:16 U.S.C. 1131-1136). The refuge also contains 345 km
of roads, most of which were developed to support two oil and gas fields that began production
in the 1950s. In this study, we sought to evaluate the relationship between road-associated
metal contamination and amphibian abnormalities. To do this we employed a stratified random
design to select sites for study.
Sites were chosen randomly to enable us to make inference about the effects of paved and
gravel roads on nearby wetlands. We had planned to limit the sites chosen for study to those
on Refuge lands, but soon recognized that doing so would not give us adequate replication of
the different road types, especially paved roads. We therefore broadened the search criteria to
all lentic wetlands on public lands within 1 km of a road in a much larger area of the Kenai
Peninsula (Figure 2).
We stratified potential study sites by road type and spatial location. We stratified on road type
(paved or gravel) because metal fate and transport mechanisms may differ between gravel and
paved roads. We also divided the study area into 5 somewhat arbitrarily drawn spatial sectors
to ensure interspersion of sites on the landscape (Figure 2). These sectors varied in road density
and traffic as well as land use and degree of urbanization. Most sites were on public lands
(including state, federal, and municipal) due to requirements for sampling permission.
Our search criteria for possible study sites included all lentic wetlands on public lands within 1
km of the road in a larger area of the Kenai Peninsula. The area chosen for study specifically
reached from Cooper Landing on the Sterling Highway to the intersection of the Sterling
Highway with the southern end of Cohoe Loop Road (Figures 2 and 3). Within this area, we
included all roads classified as a street or main road on the Kenai Peninsula Borough’s (KPB) GIS
“Roads” layer (available: http://www.borough.kenai.ak.us/GISDept/Downloads.html). We
created a 1 km buffer around the streets and main roads in this layer (Figure 3), and chose
possible wetlands only from within that buffer. The effects of road contamination on the
surrounding environment are thought to be negligible beyond a 1 km distance (Trombulak and
Frissell 2000); we therefore did not consider sites >1 km from roads for this study.
13
Figure 2. Map of Alaska, Kenai Refuge Boundaries, and study area delineated by Sector
Boundaries in red.
Each spatial sector we drew on the Kenai Peninsula is described briefly here and shown in
Figure 3. The sector with the highest road density Town included residential and industrial
areas of the towns of Kenai and Soldotna. The Town sector was the most urbanized, and sites
adjacent to roads did not always lie upon clear gradients from a road into open space or
wilderness, as was common in other sectors. In Town, one of the paved road sites (KNA90) was
chosen non-randomly because we had studied this site since 2004 and wanted to retain some
sites with historic data. The next two sectors North Spur and K-BeachCoho circumscribed ruralresidential areas. In addition to rural residences, NorthSpur contained a liquefied natural gas
plant, a nitrogen fertilizer plant, and other dispersed oil and gas facilities. Sites on public land
were only identified along paved roads in the NorthSpur sector. The K-BeachCoho sector also
14
included rural residential areas and limited oil and gas drilling operations. The next sector,
SoldSterCoop included the stretch of the Sterling Highway, Skilak Loop Road, and Mystery Hills
Road, from Soldotna to Cooper Landing. This sector included both paved and gravel roads
through rural residential areas near Soldotna, Sterling, and Cooper Landing as well as open
space areas managed as KNWR lands. One site in the SoldSterCoop sector (KNA111) was not
chosen randomly; this site was also retained because we had historic data. The final sector
Refuge Dirt contained only dirt roads within refuge lands. Most of these roads were built to
support oil and gas development, although some also provide recreational access. All wetlands
chosen in this sector had been studied extensively since as early as 2000 (Reeves et al. 2008,
Reeves et al. 2010, Reeves et al. 2011), and because historic data are particularly important for
understanding climate change, we retained 10 historic sites in this area. We also added two
new sites along a road built in 2010, at which baseline metals sampling was conducted before
the road was built. We originally had included an additional sector, Turnagain, which contained
11 candidate sites along the Sterling and Seward Highways. These sites were identified solely by
road surveys because GIS layers were not available. None of these sites contained tadpoles
when searched in June 2010, so none were retained for study.
We defined public lands as those on the KPB website’s “Ownership” layer (available:
http://www.borough.kenai.ak.us/GISDept/Downloads.html) held by the following entities: The
State of Alaska, The Alaska Mental Health Trust, The U.S. Federal Government, The University
of Alaska, or The Kenai Peninsula Borough. To select only these land parcels, we used the
following query in GIS (ArcMap version 9.3.1, ESRI, Redmond, WA, USA): "OWNTYPE" =
'BOROUGH' or "OWNTYPE" = 'FEDERAL' or "OWNTYPE" = 'MUNICIPAL' or "OWNTYPE" = 'STATE'.
After filtering all possible sites by their spatial location, proximity to road, and land ownership
in GIS, we chose potential wetlands with a combination of GIS layers and visual assessment of
digital aerial photographs. In some areas of the Kenai Peninsula, wetland habitat has been
mapped (available http://www.cookinletwetlands.info/ ). In mapped areas, we used this
wetlands layer in GIS to help us identify potential sites. Where wetlands were not mapped, we
relied on a visual assessment of digital aerial photographs to identify potential wetlands. Sites
were identified as potential amphibian breeding areas if they appeared wet, marshy, or
meadow-like in air photographs, and if the open water in these areas was ≤100 m in length. We
identified 122 candidate sites by these methods.
15
Figure 3. Study Area with sector boundaries and streets and main roads used for site selection
and 1 km buffer around them highlighted green.
Within each sector, we set target numbers of sites for inclusion in the study. All candidate sites
were then randomized within each sector. Sometimes, several ponds were clustered tightly on
the landscape within a sector. When multiple ponds were in the same area, we first
randomized the areas then assigned individual wetlands random numbers within each area. If
the first-ranked wetland in an area did not contain tadpoles or otherwise would not work, we
moved to the next randomly ranked area. We revisited areas only when they were ranked as
16
the next lowest random number in that sector. Table 1 presents the numbers of sites originally
identified and ultimately chosen for study by sector.
Table 1. Summary of Candidate and Final Study Sites by Road Type and Sector
Sector
KBeachCoho
KBeachCoho
NorthSpur
RefugeDirt
Turnagain
SoldSterCoop
SoldSterCoop
Town
Town
Total
Road Type Potential Sites Sites Recommended
Gravel
14
Paved
13
Paved
11
Gravel
18
Paved
11
Gravel
15
Paved
20
Gravel
5
Paved
15
122
5
3
3
12
0
3
7
3
5
41
Sites were visited by field crews in July of 2010. The sites with the lowest-ranked random
numbers in each sector were searched first to determine whether they met criteria for
inclusion in the study. A site that met the following criteria was included in the study: the site
was safe and accessible, it contained standing water in July, and frogs or tadpoles were found.
We added wetlands to the study in each sector in order of their randomly-assigned numbers. If
the site was unsafe, not accessible, dry, or frogs were not found after dip-netting the entire site
perimeter, that site was dropped from the survey and crews moved to the next site in order of
the random numbers assigned.
We identified 29 of 36 study sites randomly, using these procedures, after which we were
forced to select some sites that did not meet all field selection criteria. We chose these
additional sites non-randomly, and used them to ensure we had a good balance in the distance
from each site to the nearest road. When we checked this factor in the randomly-chosen sites,
the average distance from sites to roads varied among the sectors and road types, in a way that
would have limited inference. We therefore intentionally identified candidate sites that we had
rejected earlier because they did not have frogs or because access was problematic, and
decided to accept some sites that did not meet all criteria, but were closer to the road in some
sectors or farther from the road in others.
During the 2010 pilot year of this study, we sampled 6 sites intensively to determine the
sampling strategy for the 36 study sites sampled during 2011 and 2012 (Figure 4).
17
Field methods
Since 2000, Service staff have examined over 9,000 metamorphic wood frogs from 59 sites for
abnormalities at the Kenai Refuge following national monitoring protocols (protocols available
at: http://datadryad.org/resource/doi:10.5061/dryad.dc25r). This number includes historic data
and the examination of 3,295 animals at 36 sites between 2010 and 2012 for this project. Field
activities at each of these sites included instrumentation with deep and shallow continuous
temperature loggers, biweekly monitoring of water quality, precipitation, site length and width,
water depth, and tadpole development. Additionally, each site was assessed for amphibian
abnormalities, sampled for the presence of the fungal pathogen, Batrachochytrium
dendrobatidis (Bd), in water and frogs, and habitat characteristics were measured. The
following media were sampled for metals: snow, water, sediment, soil, dust, and tadpoles. Six
sites were instrumented with the following continuous data loggers in 2010–2012 to provide
thorough examination of variation in these parameters: Temperature, Conductivity, Depth, and
Rain. These continuous data were collected to augment the same data gathered roughly
biweekly at each site from April through August each year of the study. This biweekly
monitoring of sites included point measures of water quality, and depth profiles in deeper
wetlands. Table 2 summarizes the data collected for this project and its spatial and temporal
scale.
Biweekly Field Visits and Amphibian Monitoring
Field personnel visited each site once every two weeks to gather information about the size of
the site, the amount of precipitation, weather conditions, breeding and metamorphosis dates
and the rates of amphibian development at each site. When eggs were present, the number of
egg masses was enumerated, and the presence of dead eggs was noted. When tadpoles were
present, between 8 and 10 were captured with hand-held dipnets and staged according to
Gosner (1960) to monitor rates of development and predict timing of metamorphosis. During
metamorphosis, a target of between 50 and 100 metamorphic wood frogs (L. sylvaticus) were
captured and assessed for abnormalities (as described below). In addition to monitoring
general site characteristics, the site rain gauge and staff gauge were checked and their readings
recorded. Site length and width were measured with a laser range finder and water quality
measurements were taken at the surface and at depth as described below.
Metamorphs and Abnormality Assessments
Methods for metamorph abnormality assessment are fully described in the USFWS standard
operating procedures, (Available at: http://datadryad.org/resource/doi:10.5061/dryad.dc25r).
Briefly, 50-100 metamorphic frogs, stage 42–46 (Gosner 1960), were assessed for abnormalities
at each site. This method controls tadpole stage by limiting animals sampled to recent
metamorphs between forelimb emergence and complete tail resorption. Snout-to-vent length
18
(SVL) and tail length were measured, and developmental stage recorded. All abnormalities were
classified by a single researcher using the USFWS SOPs. All frogs were released at the capture
site, after abnormalities were photographed. We followed guidelines for the use of live
amphibians outlined by the American Society of Ichthyologists and Herpetologists (ASIH 2004)
when handling and sampling specimens. All animal collections were authorized under sampling
permits approved by the State of Alaska, Department of Fish and Game. On a subset of
metamorphs we collected the following information.
o Conservation genetics—40 animals from each site were sampled for
conservation genetics analysis. Using a foam-tipped applicator each metamorph
was swabbed along its dorsal side 15 times in each direction (head to tail, tail to
head) for a total of 30 strokes. Each swab sample was preserved in 600 µL of
Longmire buffer in a 1.7 ml sterile microcentrifuge tubes. Samples were sent to
Sandra Talbot (USGS, Alaska Science Center) for storage until funds can be found
for analysis.
o Perkinsus like organism (PLO)—A disease of recent conservation concern has
been documented on the Kenai NWR. It is a protozoan pathogen, taxonomically
similar to the saltwater mollusk endoparasite, Perkinsus marinus, which causes
die-offs in marine invertebrates, and also members of the taxonomic grouping
Amphibiothecum, which includes multiple species of dermocystidium and
dermosporidium, both parasites of frogs. Frogs and tadpoles with this disease
swell up (first the liver gets white and swollen, then all viscera get swollen,
including the heart, which swells into a round ball, which you can see beating in
the animal’s throat in the worst cases).
Water Quality and Contaminants Sampling Summary
To assess site characteristics, we measured water quality, collected samples of site water,
sediment, tadpoles and periphyton for laboratory analysis. To assess road characteristics and
mechanisms of metal transport from roads of different types, we measured metals in the
following media at fixed sample points 5 and 50 m from the road. Figure 5 shows a schematic of
our sampling strategy and Table 2 shows the temporal scale of sampling.
19
Figure 4. Map of Kenai Study Sites.
At each site, we measured water quality roughly biweekly and conducted three collections of
water samples for dissolved metals, anions, and dissolved organic carbon analysis in late April /
early May, June and August, of 2011 and 2012. During each of these water sampling events,
water at depth was also collected at those sites greater than 1 meter deep. In 2010, samples
and water quality measures were taken at the edge of the wetland nearest to and farthest from
the road. Analyses of these data showed that in most sites waters were well enough mixed that
it did not justify the cost of retaining this design, so in 2011 and 2012, samples and water
quality measures were only taken at the “near” edge of the site. In 2010 we compared the
20
results of sampling sediments from grab samples versus sediment traps, and due to lack of
significant variation between the two methods, chose to use the grab sampling method for
2011 and 2012 because it was simpler. During the 2011 season we determined that
understanding bioavailability and potential bioconcentration of metals was important to our
understanding of metals behavior in these systems and potential impacts to aquatic life. As a
result we decided to collect periphyton samples from each site for metal analysis and tadpole
tissue samples. Additionally water samples from each site were collected to analyze for the
presence of the chytrid fungus Batrachochytrium dendrobatidis (Bd). Adult wood frogs were
also opportunistically sampled via swabbing when discovered during site visits.
On the road transects in 2011 and 2012, samples were taken at fixed 5 meter and 50 meter
transect points from the road to determine what metals were road-associated in what media
and enable us to infer mechanisms of contaminant transport. Samples during the 2010 pilot
year were taken at a 5 point transect of 0, 5, 10, 20, 40, and 80 m from the road. From these
data, we saw that metals concentrations in the media we sampled dropped substantially in the
first 50 m from the road, and therefore chose 5 m as an “impact” sample, and 50 m as a
“reference” sample for each site. These points were marked and samples then were taken at
the same locations in 2011 and 2012 to assess interannual variation in these parameters. Snow
samples were collected the first week of April, 2011 and the last week in March, 2012, and dust
samplers were deployed at these same transect points and collected from each site during the
same two week period in the first half of June. Deployment and collection of dust samplers was
consistently conducted over a two-day period to make the sampling effort across sites as
consistent as possible. Sediment samples were collected in June. Soil samples were collected in
August at the same transect points used for snow and dust sampling.
A summary of the analysis of the 2010 pilot study is available with this data submission,
“2010PilotStudyRecommendations.pdf” and detailed field methods follow.
Water Quality and Quantity Measurements
We measured the following variables at a frequency of once every 14–21 days at each study
site between the end of April and mid-September.
 Temperature
 pH
 Conductivity
 Dissolved Oxygen
 Turbidity
 Chloride
 Water level (staff gauge reading)
 Precipitation (rain gauge volume collected since previous site visit)
21
All water quality measurements were taken with a YSI 6820V2 water quality sonde, calibrated
at least bi-weekly for all parameters. Measurements were taken at an established point on the
edge of the wetland nearest the road (Figure 5), within 1 m of the temperature logger. The first
measurement was taken at 10 cm depth, after which recordings were made at 30 cm
increments until the YSI reached the bottom of the wetland. We also took readings on the staff
gauge and rain gauge during each site visit and assessed tadpole development.
Water Sampling
To maintain consistency across sites, water samples were collected at the edge of the wetland
closest to the road. This location was established at the beginning of the season during
temperature logger deployment, and water quality measurements were repeated at this
location throughout each season. Each sample was collected at the wetland edge and the
sampler took care to not disturb wetland sediment from the site bottom into the water column.
In shallow wetlands, samples were collected from 10 cm below the surface. In deep wetlands,
samples were also taken 10 cm above the wetland bottom using a 12” subsurface grab sampler
device fitted with a chemically-clean HDPE plastic bottle for each sample
(http://wheaton.com/catalogsearch/result/?order=relevance&dir=desc&q=990400 Wheaton
Industries Inc., Millville, NJ, USA). Locations of deep samples were at least 1 m below the water
surface, because the point of these samples was to obtain water sampled for metals in a
reducing environment at depth, to determine how redox potential (which varies with depth at
some sites) affected metals solubility in this system.
Each water sample was collected in a chemically clean 1 liter HDPE bottle. The system we used
to filter the samples for dissolved metals, anions and dissolved organic carbon used a peristaltic
pump head driven by a hand-held drill. The pump head was connected to a 0.45 micron high
capacity ‘GWV’ capsule filter using platinum cured silicone tubing. Each sample filter was
flushed with 1 liter deionized water followed by 1 liter of site water prior to sample collection.
Water was then filtered from the 1 liter sample bottle into each of the analysis-specific sample
bottles. Water quality was recorded with the YSI meter concurrent with sample collection.
Titration tests for alkalinity were conducted on filtered site water concurrent with water
sample collection.

Filtered sample types (late April/early May, June , August)
o Dissolved metals—Samples were collected in chemically-clean 60ml HDPE
bottles. Bottles were rinsed 3 times with filtered water prior to sample
collection. Samples were acidified with 100 µL of concentrated high purity Ultrex
16 molar nitric acid.
22
Figure 5. Schematic of data logger deployment and sampling types and locations for
the Kenai wetlands and wood frog study.
Road
Road
5m
Shoulder
Temperature logger deployment site – each logger will be anchored to the
wetland edge using a wooden stake and tethered with a plastic covered
cable, a float will be used to suspend the logger at the proper depth and
shade it from direct sunlight.
Water quality monitoring and depth profile station (within 1 meter of
temperature logger), site visits and measurements will be taken every 2 -3
weeks.
Water sample point at which sampling occurred 3 times per season with
a volume of approximately 1 liter collected during each sampling event.
Sediment sample point, sampling will occur 1 time per season with a
volume of less than 1 liter by volume of material collected.
Transect points for snow, soil, and dust samples, sampling for each media
will occur once per season. Snow and soil samples will be less than 1 liter
by volume of material collected per transect point. Dust will be a minimal
amount of material, we are hopeful that several grams can be collected per
transect point.
Staff gauge site
Rain gauge site
23
50m
Figure 5. Schematic of locations of media sampled for metals with respect to road and
wetland.
o Anions—Samples were collected in chemically-clean 40ml HDPE bottles. Bottles
were rinsed 3 times with filtered water prior to sample collection. No acid was
added to these samples care was taken to leave no head space in the sample
bottle.
o Dissolved Organic Carbon (DOC)—Samples were collected in 4 oz. baked amber
glass bottles secured with Teflon lined caps following USGS protocols, which
stipulate that bottles not be rinsed prior to sample collection. Samples were
acidified with 100 µL of concentrated high purity 12-molar hydrochloric acid.

Unfiltered water samples (June)—These samples were taken to increase the level of
comparability between this dataset, the Kenai historic dataset, and the Anchorage
dataset, both of which report results of unfiltered water samples analyzed by these
methods. Results of the ICP-MS analysis for the unfiltered samples were predicted to be
higher than the filtered samples, which represented the dissolved fraction only. During
the mid-summer water sampling event we captured different sources of metals to
wetland biota, by collecting samples that enabled us to quantify the difference between
filtered and unfiltered samples (dissolved and total metals) that were co-located in time
and space (see “2010PilotStudyRecommendations.pdf”). The sampling and analysis
methods we used to obtain the total metals fraction was intended to dissolve the
organic matter and sediment and report inorganic materials sorbed to these organic
materials in the water column. Samples for total metals underwent a more aggressive
extraction procedure, intended to dissolve the sediment or other organic matter in the
sample.
o Total metals—Samples were collected in chemically-clean 60ml HDPE bottles
and acidified with 100 µL of concentrated high purity Ultrex 16 molar nitric acid.

Water sampling for Batrachochytrium dendrobatidis (Bd) (once per season)—This is a
fungal pathogen suspected of causing widespread amphibian declines and we sampled
site water for presence of the pathogen. Water was passed through sampling filters
following USGS protocols to assess for the presence of Bd at each site.
Contaminants Sampling in other Media
 Sediment samples were collected for metals analysis once per year at times that
tadpoles may plausibly have burrowed or fed in wetland substrate. To determine the
24
potential for sediment as an exposure route to metals contamination we collected a
sample at each site. At shallow sites this was a grab sample collected using a gloved
hand while at deeper sites an Ekman dredge was used to collect the sample. The sample
was collected in a 1gallon Ziploc bag and thoroughly mixed to ensure homogeneity and
submitted to the lab for analysis.

Transect samples for metals (once per season)
o Snow (pH, anions, ICP metals)—We sampled snow because it has been shown to
have low pH and high dissolved metals content in some places, due to road
inputs. We sampled snow on a transect by using a chemically-clean plastic scoop
to fill a 1 quart Ziploc bag with snow at points 5 and 50 m from the road edge, as
shown on Figure 5. Snow was collected such that the entire depth of the
snowpack was incorporated into the sample. We collected snow in late March to
early April to capture as much of the season’s snowpack as possible but before
break-up and snowmelt had progressed significantly.
o Soil (ICP metals)—We collected soil samples to determine how the inorganic
content of mineral soil changed with distance from the road, both to understand
the parent geology of the upper soil horizon adjacent to the wetland, and to see
whether a road signature was present in soils, and if so for what distance from
the road it persisted. For this sampling, we collected mineral soil once per season
in mid-summer using a chemically-clean plastic scoop to fill a 1 quart Ziploc bag
at transect points 5 and 50 m from the road edge, as shown on Figure 5. Soil
samples were composite samples at each location, consisting of four subsamples
taken at the cardinal directions 1 m from the transect point marker.
o Dust (ICP metals)—Dust has been shown to be a source of road-based metal
contamination to wetlands. We collected dust samples for analysis once per year
by deploying passive dust sampling devices after snow melted in the spring, for a
fixed time interval of 2 weeks, so we could compare dust across sites. Dust and
any precipitation that had accumulated in the collection container were
transferred to a chemically clean hand pumped vacuum filter holder. At the end
of the sampling period, the contents were drawn through the filter, and the filter
was submitted for analysis. Dust was analyzed for total mass and organic and
inorganic content at the analytical lab.

Road maintenance material associated with multiple sites for metals—Gravel and salt
mixtures used for winter road maintenance may be additional sources of metals to
wetlands. We worked with the Alaska Department of Transportation in Soldotna, to
sample these road maintenance materials in 2010. One composite sample was collected
in 2010.
25

Primary productivity and periphyton samples for metals (once per season)—We
sampled periphyton as a measure of primary productivity for a fixed period during the
summer to compare across sites. We also collected periphyton for metals analysis in
2011 and 2012; however the metals analysis from the 2011 samples was not reliable
due to low sample mass. This was a methodological issue we corrected in 2012, leaving
the study with one year of data (2012) for this parameter. The 2011data are included in
the data submission but are of questionable quality, which we urge you to discuss with
the authors of the data submission prior to use or interpretation of these results.

Tadpole tissue samples for metals (once per season)—Given the high levels of certain
metals, particularly copper, in some of the 2010 water samples from certain sites, and
our experimental work implicating copper as interfering with predation dynamics
between tadpoles and dragonflies in these systems (e.g. Reeves et al. 2011 and Hayden
et al. in prep), we decided to test tadpole tissue for metals in 2011 and 2012. To do this
we collected 5 tadpoles from each site to create a composite sample totaling at least 1
gram wet weight. Tadpoles were euthanized in liquid nitrogen and preserved frozen but
unfixed for metals analysis.
QA/QC for sampling
All analytical sampling incorporated a number of duplicate samples equivalent to 10% of the
total samples taken. For each round of water sampling there was also a trip blank and field
blank tested to assess for the possibility of sample contamination. Samples were stored
refrigerated or frozen as appropriate prior to transportation to the laboratory facility. From
time of sample collection to transfer to the lab was usually 5 but no more than 14 days;
samples were hand delivered to lab personnel and the chain of custody documented. All
samples submitted for analysis included a hard-copy and electronic sample inventory check by
field and laboratory staff. For samples analyzed at out-of-state laboratory facilities samples
were shipped directly to the researcher conducting the work by expedited delivery maintaining
storage recommendations for those samples.
Data-Logger Measurements
We measured temperature at hourly intervals with HOBO tidbit data loggers (Onset Computer
Corporation, Cape Cod, MA, USA, http://www.onsetcomp.com/products/hobo-dataloggers/waterproof) between late April (when data loggers were deployed) and mid-September
(when loggers were collected). At all sites one logger was deployed at a depth of 10 cm below
the water surface. Deeper sites (>1m depth) had a second logger deployed at a depth of 10 cm
above the bottom of the wetland. The temperature loggers were floated below the water
surface, suspended beneath a piece of foam floating to maintain a consistent logger depth. The
26
loggers and float were secured to an anchor at the pond edge with a plastic-covered wire cable.
The location for the temperature loggers at each site was the edge nearest to the road. The
distance from this near edge to the road was determined with a laser range finder or GPS track.
The conductivity loggers, water level loggers and tipping bucket rain gauges, six of each type,
deployed at the 2010 pilot sites were co-deployed at the following six sites in 2011/2012: 90,
97, 1024, 1034, 1069, and 1090. The conductivity logger was suspended in a perforated PVC
pipe to protect it while still allowing water movement around the device. The water level logger
was suspended in a stilling well, constructed with PVC pipe. The tipping bucket rain gauge was
fixed to a concrete pedestal at a point adjacent to the wetland in an open area where it was
clear of any vegetation that could impede the free fall of precipitation.
Habitat Assessments
Habitat assessments were conducted by our collaborators at the University of Alaska
Anchorage, Environment and Natural Resources Institute (ENRI). Littoral habitat
characterization was based on a 15 m-wide plot that extended 10 m into the wetland at each
habitat station. They measured water depth 10 m from the shoreline; noted the presence of
any surface scum, algal mats, or oil slicks; and noted the sediment color and any odor. Within
the plot they characterized the coverage of inorganic and organic substrates, aquatic
macrophytes, and fish cover. Inorganic substrate coverage was classified separately for each of
6 particle size classes: bedrock (>4000 mm), boulder (250–4000 mm), cobble (64–250 mm),
gravel (2–64 mm), sand (0.06–2 mm, gritty), and fines (<0.06 mm, not gritty). For organic
substrates they classified the coverage of both woody debris and other organic detritus.
Aquatic macrophyte coverage was classified for each of three growth forms: submergent,
emergent, and floating. For fish cover, the coverage of inundated herbaceous vegetation,
woody debris, inundated live trees, overhanging vegetation, ledges or drop-offs, boulders, and
human-made structures were each classified. The areal coverage classes used for littoral and
riparian habitat components were: absent, sparse (<10%), moderate (10-40%), heavy (40-75%),
and very heavy (>75%).At each habitat plow they also classified riparian vegetation for a 15 m x
15 m plot adjacent to the water’s edge. They classified vegetation in both the canopy
(vegetation >5 m high) and understory (vegetation 0.5–5 m high) as deciduous, coniferous,
mixed, or absent. For the canopy, they then classified the coverage of both big trees (>0.3 m
diameter at breast height [dbh]) and small trees (<0.3 m dbh). In the understory, they classified
the coverage of both woody (shrubs and saplings) and non-woody (herbs, grasses, and forbs)
plants. In the ground cover (features < 0.5 m high) they classified the coverage of woody
plants, non-woody plants, standing water/inundated vegetation, and barren surfaces/buildings.
27
Invertebrate Community Assessments
Our collaborators at the University of Alaska Anchorage, Environment and Natural Resources
Institute (ENRI) also conducted macroinvertebrate sweeps to assess the invertebrate
community at each site. They sampled aquatic macroinvertebrates and characterized habitat
conditions at the USFWS Kenai Peninsula study ponds (7 ponds in 2010, 32 ponds in 2011, and
34 ponds in 2012). The work followed USEPA’s National Lakes Inventory and Monitoring
protocols (USEPA 2007), in which macroinvertebrate sampling and habitat characterization
were conducted at 10 stations (i.e., stations A – J) evenly spaced around the pond’s perimeter.
They sampled macroinvertebrates by taking a one-meter-long sweep with a 500-µm-mesh Dframe net through the dominant habitat found at each station. They combined the 10 sweeps
from a given pond into a composite sample, which they preserved in the field with 70% ethanol.
In the lab, they randomly subsampled 500 organisms from each invertebrate sample and
identified each to the lowest practical taxonomic level, generally genus, subfamily, or family
depending on the taxon and its condition. They visually scanned the remainder of the sample
to remove large or rare individuals and all predators (i.e., dragonfly and damsel fly larvae
[odonates] and predaceous diving beetles [dytiscids]), which they identified and enumerated
(Raw data files are uploaded as a separate excel file to preserve formatting). From these
samples they documented three new aquatic insect records for Alaska, which were included in
a journal publication (Rinella et al. 2012). These were the mayfly Caenis youngi and the
caddisflies Philarctus bergrothi and Sphagnophylax sp.
IV. RESULTS
Description of Data
The data for this project were collected but not analyzed. They have been shared online
through the digital repository datadryad.org, as an attachment to our 2010 paper, Multiple
Stressors and the Cause of Amphibian Abnormalities (Reeves et al. 2010). Below information
about each of the files is compiled here to consolidate this information in one place for
potential users of the dataset. With this data submission, we include data from three separate
but related studies: Historic Kenai data, which were reported upon in Reeves et al. (2010),
Kenai data from the 2010–2012 Kenai follow up Multiple Stressor Study, and data from a pilot
study conducted to assess the effects on roads on water quality of wetlands in Anchorage.
These studies and the data associated with them are described below.
Kenai Data (Historic Studies)
We have collected information on abnormal amphibians and their causes at the Kenai Refuge
for over a decade and include all these data with this submission. Since 2000, we have
28
examined over 9,000 metamorphs from 59 field sites in road-accessible and remote locations
for abnormalities at the Kenai Refuge. During a prior study (completed in 2007 and published in
2010), we measured temperatures, invertebrate populations, and chemical contaminants. The
2010–2012 data may be used to vet the results of the prior (2010) analysis and test hypotheses
about mechanisms postulated by the experimental results (Reeves et al. 2010, 2011, and
Hayden et al. in prep).
Figure 6. Locations of current and historic study sites in Kenai with relationships to roads and
traffic. AADT=Average annual daily traffic data from the Alaska State Department of
Transportation. Available at: http://www.dot.state.ak.us/stwdplng/mapping/adt.shtml
Kenai Data (Present Study)
This is the most comprehensive and fine scale data set. Collected between 2010 and 2012, it
includes data from 36 study sites sampled randomly within the Kenai Peninsula, adjacent to
gravel and paved roads spanning a range of traffic intensities (Table 2 provides a summary of
Kenai data).
29
Table 2. Spatial and Temporal Scale of Project Data.
Parameter
Measured
Sites
Media
Sampling Frequency
(per year)
Conductivity
6
water
Hourly loggers
Temperature
36
water
hourly
Water quality
(pH, DO,
conductivity,
turbidity, and
chloride)
Rain
36
water
every 2-3 weeks
36
NA
Water Depth
36
NA
Width and
Length
Tadpole growth
36
NA
Gauges checked every
2-3 weeks
Gauges checked every
2-3 weeks
every 2-3 weeks
36
tadpoles
every 2-3 weeks
Metals dissolved
36
water
3x per year
Dissolved
Organic Carbon
Hardness
36
water
3x per year
36
water
3x per year
Anions
36
water
3x per year
Alkalinity
36
water
3x per year
Metals total
36
water
Metals
36
Sediment
1x per year - second
sampling
1x per year
30
Sampling
intensity per
site?
1 location per
site
1 Shallow (and
1 Deep at sites
>1m deep)
1 location per
site, on depth
profiles
1 location per
site
1 location per
site
1 location per
site
10 tadpoles
checked
Shallow (and
Deep at sites
>1m deep)
Shallow and
Deep
Shallow and
Deep
Shallow and
Deep
Shallow and
Deep
Shallow and
Deep
1 composite
sample
Metals and
anions
36
snow
1x per year
Abnormalities
36
site
1x per year
Dead Egg masses
36
site
1x per year
Bd
36
water
1 x per year
Genetics
36
metamorphs
1 x per year
Metals
36
tadpoles
1 x per year
Metals
36
soil
1 x per year
Habitat
Metals
36
36
site
dust
1 x per year
1 x per year
Invertebrates
Bd
36
opportunistic
site
adults
1 x per year
ongoing
5 and 50
meters from
road
Metamorphs
assessed
Enumerated
during site
visits for water
quality
2-3 filters at
most sites
40 per site
swabbed
5 tadpoles
(pooled)
5 and 50 m
from road
Site level
5 and 50 m
from road
Site level
variable
Anchorage Data (2009 Project)
Although this data set is the most limited, it may also be useful to put our results on roads and
water quality into context because it sampled a much more urbanized area. These data were
collected during a pilot study in 2009, the objective of which was to assess the efficacy of
constructed wetlands for metals reduction during storm water treatment and to assess the
effect of routing storm water into natural and unconstructed wetlands in the municipality of
Anchorage. It includes 17 wetlands (7 constructed, 7 non-constructed but receiving storm water
inputs of varying magnitudes, and 3 reference sites). Sites were near paved and gravel roads of
varying traffic intensity.
31
Table 3. Summary of spatial and temporal scale of Anchorage wetlands and roads study data.
Analytes
Sites
Media
Time Series
Metals
Metals
17
17
Sediment
water
n
n
Water
quality (pH,
DO, salinity,
TDS)
17
water
n
Sampling
Frequency
Once
Once
1x per month
- 2 times total
Sampling intensity
per site?
1 composite sample
composite, inlet and
outlet where relevant
1 location per site, on
depth profiles
Figure 7. Map of current Kenai study sites with Anchorage sites and road traffic data.
AADT=Average annual daily traffic data from the Alaska State Department of Transportation.
Road data available at: http://www.dot.state.ak.us/stwdplng/mapping/adt.shtml
32
Figure 8. Map of Anchorage roads and wetlands water quality study sites.
Site Locations
SiteLocations.csv
Locations of all sites in South-central Alaska at which either amphibian data or water quality
data were gathered are provided in the SiteLocations.csv file. This file contains location
information for all wetland sites at which either at least one frog was examined or water quality
measurements were taken. Limited water quality and metals data are available for sites in
Anchorage. Some sites in Kenai only have frog abnormality data and link to the
“abnormality.csv” dataset.
Column information follows:
SITE_ID—This is the link field to the other tables in this dataset. It is the identifier for each
wetland site in the survey.
LATITUDE—The latitude that was logged on field forms and entered into the database. Data are
referenced to the NAD83 datum.
LONGITUDE—The longitude that was logged on field forms and entered into the database. Data
are referenced to the NAD83 datum.
33
Area—The name of the part of Southcentral Alaska the site was in, this may be a useful sort
field for some analyses.
Site Monitoring
SiteEvents.csv
Sites were monitored roughly biweekly to gather data for the project. Information about sites is
included in the SiteEvents.csv file.
File Information
The “SiteEvents.csv” data set includes monitoring data that were collected approximately every
14 days during the summer months.
Column Information
siteEvent_ID—This column is a join field to the “WaterQuality.csv” file, which contains multiple
water quality measurements at each wetland site (e.g. along depth profiles).
Site—This is a join field to the “SiteLocations.csv” file which includes the latitude and longitude
of the site.
Date—The date on which monitoring occurred.
Width_m—The width of the water body in meters.
Length_m—The length of the water body in meters.
RainGauge_mm—The reading on the rain gauge at the site in millimeters.
StaffGauge_cm—The reading on the staff gauge at the site in centimeters.
Weather—The observations of field staff about weather conditions during the visit.
Wind—The observations of field staff about wind strength during the visit.
Amphibian Data
Data on amphibians span the time period 2000–2012. Information on abnormalities, breeding
and metamorphosis dates, rates of development, and disease are provided.
Breeding and Metamorphosis Dates
BreedingMetamorphosisDates.csv
Data in the BreedingMetamorphosisDates.csv file describe the developmental period of
Lithobates sylvaticus tadpoles in study sites from 2010–2012.Breeding dates were not as well
documented prior to this study, but metamorphosis dates may be obtained from collection
dates in the “FrogAbnormalities.csv” file.
Column Information
34
Site—The site at which the observations were made. This is a join field to the
“SiteLocations.csv” file.
Year—The year in which amphibians were assessed.
EggDates—The date on which egg masses were first noted at the site. Field crew members
visited each site biweekly (“methods.csv”) so there is error in this estimate if it is considered to
be a breeding date.
CollectionDates—The field crew examined metamorphic wood frogs (between Gosner (1960)
stages 42 and 45 on this date).
DeadEggsNoted—Field crews were instructed to note when they observed dead egg masses at
a site. This binomial field indicates whether they did observe dead eggs (1) or they did not (0) at
that site that year.
Frog Development
FrogDevelopment.csv
The FrogDevelopment.csv table describes each site visit during the study, whether frogs were
found and their developmental stage, and how much time was spent assessing the site and
performing the frog survey.
Site—The wetland at which data were collected. This provides a join field with the site table.
Date—The date on which the site visit occurred.
EggMassesPresent—This is a true false field describing whether egg masses were seen (1) or
not seen (2).
NoEggMass—A count of the approximate number of egg masses. Best estimates were made
without moving or disturbing the eggs.
EggMassDepth—The approximate depth from the water surface to the top of the egg mass
cluster is reported.
Tadpoles—This is a true false field describing whether tadpoles were captured (1) or not
captured (2) for field examination.
MeanGosner—The mean developmental stage of the 8-10 tadpoles captured (“methods.txt”).
SiteTime—The amount of time spent at the site, including the amphibian survey and any water
quality or metals sample collection.
FrogSurveyTime—The amount of time spent conducting the amphibian survey alone. This is a
measure of how long it took 2-4 field staff to capture 8-10 tadpoles for examination.
35
Fish—This is a true false field describing whether fish were observed (1) or not observed (2).
This is somewhat anecdotal because survey methods were not designed for fish.
Comments—Observations made by field staff during the site visit. These are anecdotal in
nature and do not represent the views of the US Fish and Wildlife Service.
Frog Abnormalities and Disease
FrogAbnormalities.csv
The FrogAbnormalities.csv data set includes information on each of the 9,011 individual
amphibians surveyed for this effort from 2000–2012. Information on data in each column
follows.
COLLECTION_ID — The collection ID always follows the same format, ("site_id”-”species ”“date" in the format, “MMDDYY”). All amphibians sampled for this project were Lithobates
sylvaticus (formerly Rana sylvatica or RASY), based on the following reference, Crother B (2012)
Scientific and standard English names of amphibians and reptiles of North America north of
Mexico. Saint Louis, MO: Society for the Study of Amphibians and Reptiles.
FROG_ID—This combines with the collection_id field to create a unique identifier for each
individual amphibian sampled.
SITE—The identifier for each wetland in which an animal was sampled. This field provides a join
field to the site.csv file.
YEAR—The calendar year in which the animal was sampled.
GOSNER_STAGE—The developmental stage of the animal, based on Gosner (1960). All animals
were “metamorphs”, between stages 42 and 46. “NULL” codes for missing data.
SVL—The length of the animal from snout (tip of nose) to vent (cloaca), measured in mm. In
animals with tails, SVL does not include tail length. “NULL” codes for missing data.
TAIL_LENGTH— The length of the animal’s tail from the vent (cloaca) to the tail tip, measured in
mm.
DATE—The date on which the animals were sampled.
For more information on the following categories, please refer to the “Abnormality
Classification SOP” document, which is included with this data submission. Each of these
columns includes a binomial (0 or 1) response variable coding for whether the animal had an
abnormality of each of these specific types (“1”) or not (“0”). The first 10 categories are data
summarized from the SOP columns as follows:
ABNORMAL—Includes all categories of abnormalities in the SOP. These are the final columns of
the spreadsheet, which are described in detail in the “Abnormality Classification SOP”
document also included with this data submission - Table 1 of this SOP document links the
names in the databases to the descriptive categories in the SOP. It was possible to have more
than one type of abnormality. In this case, it would receive a count for each SOP level
36
abnormality it had, but all the summary categories above this in this document count abnormal
animals, not individual abnormalities. For example, and animal with a shrunken limb and an
abnormal eye would have a "1" in each of the SOP level columns, however, it would only be
counted once in the "skeletal_plus_eye" category.
EYE_AB—Includes the following categories of abnormalities:
abnormal_iris_coloration
abnormal_eye_size_shape_iris_or_pupil
anopthalmia
eye_abnormality_other
BLEEDING_INJ—Includes the following categories of abnormalities:
amelia_full_remove_bloody
appendage_dislocated_broken_dangle
brachydactyly_bone_protruding_or_blood_at_stump
ectrodactyly_bone_protruding_or_blood_at_stump
ectromelia_bone_protruding_or_blood_at_stump
digits_curled_smashed_with_blood
skeletal_injury_other
bifurcated_tail
pooled_blood
intestines_protruding
trauma_or_cut
SKEL_AB—Includes the following categories of abnormalities:
brachydactyly
ectrodactyly
ectromelia
kinked_tail
non_flexible_limb
one_limb_thinner
skeletal_abnormality_other
amelia_full_remove_bloody
appendage_dislocated_broken_dangle
brachydactyly_bone_protruding_or_blood_at_stump
ectrodactyly_bone_protruding_or_blood_at_stump
ectromelia_bone_protruding_or_blood_at_stump
digits_curled_smashed_with_blood
skeletal_injury_other
amelia
anteversion
brachynathia
37
clinodactyly
cutanuous_fusion
hemimelia
microcephaly
micromelia
polydactyly
polymelia
polyphalangy
scoliosis
syndactyly
taumelia
skeletal_malformation_other
SURF_AB—Includes the following categories of abnormalities:
bifurcated_tail
pooled_blood
edema
cysts
intestines_protruding
missing_typanum
pigment_anomaly
trauma_or_cut
surface_other
Perkinsus—Animals with a 1 in this category were diagnosed in the field with visual signs of
infection by an undescribed protozoan pathogen, similar to Perkinsus marinus. The taxonomy
of this organism has not been done and the pathology has not been verified by a lab in most
cases.
FROG_COMMENTS—These are descriptions of the abnormalities by those who examined the
animals in the field. Notes about the frogs were taken by field crews during frog assessment.
Statements are anecdotal observations by field crew members of variable levels of expertise
and training. The veracity of these statements has not been verified. These statements
represent the opinions and observations of individual workers and do not necessarily represent
the views of the U.S. Fish and Wildlife Service. Pictures are not available.
38
Water Quality Data
WaterQuality.csv
The WaterQuality.csv table describes water quality information collected by field observers
during each site visit from 2010–2012. Water quality measurements were not taken as
consistently during the prior study and may not be comparable, therefore are not shared with
this data submission. All water quality measurements were taken with a YSI 6820V2 water
quality sonde, calibrated at least bi-weekly for all parameters. Measurements were taken at an
established point on the edge of the wetland nearest the road, within 1 m of the temperature
logger. The first measurement was taken at 10 cm depth, after which recordings were made at
30 cm increments until the YSI reached the bottom of the wetland. See above for more
information on field data collection methodology.
siteEvent_ID—This is a join field between this table and the “SiteEvents.csv” table. There are
many water quality observations for each site event, due to methods including sampling on
depth profiles through the wetland and a near to and far from the road design in the pilot year
of the study (2010).
Label—a text identifier describing the water quality sampling event. This label follows the
naming convention: Site_Date_Depth of measurement in cm, i.e., “SiteID_MMDDYY_100”.
Location—During the pilot year of the study water quality measurements were taken “Near”
and “Far” from the road (the factor of interest in the study was roads and their effects on water
quality). This design aspect was dropped after analyses of pilot data showed most wetland
waters to be well-mixed enough that the cost of this procedure was not justified.
Temp_C—The temperature of the water in Celsius.
Spc—Specific conductance of the water in uS/cm.
Depth_m_reading—The depth of the sample in meters, as measured by the instrument. Water
quality measurements were taken on depth profiles at 10 cm (surface) and subsequently every
30 cm until the sonde reached the bottom of the wetland.
pH—The pH of the sample, i.e. the negative log of the hydrogen ion concentration.
Cl_ppm—The chloride ion concentration in parts per million, as measured by the sonde.
Additional laboratory-based chloride ion concentrations are available when water samples
were taken in the “AnalyticResults.csv” file.
Turbidity_NTU—Turbidity from a calibrated nephelometer measured as the propensity to
scatter light and reported in calibrated Nephelometric Turbidity Units (NTU).
DO_perc—The dissolved oxygen concentration in the water, reported as a percentage.
DO_ppm—The dissolved oxygen concentration in the water, reported as parts per million or mg
per liter.
SampleDepth—The target depth of the sample, which may be useful as a classification variable.
39
Conductivity— Conductivity measures the ability of a material to conduct electrical current,
reported in us/cm.
PercVegClass—A estimate of the percentage of the 1 square-meter area of the ground directly
adjacent to the water quality sample location that was covered by vegetation.
PercBareClass—An estimate of the percentage of the 1 square-meter area of the ground
directly adjacent to the water quality sample location that was covered by bare ground.
Salinity_ppt—The measure of salinity of the water sample in parts per thousand provided by
the water quality sonde.
Analytical Data
AnalyticAttributes.csv and AnalyticResults.csv contain all data on metals concentrations, anions,
organic carbon in all media sampled for the project and notes taken by field and lab personnel
during sampling and analysis.
Analytic Attributes
The "AnalyticAttributes.csv" File contains notes taken during field collection of the samples,
location data for transect samples, and other information that may assist with analysis of the
analytical chemistry data. The latitude and longitude attached to the snow samples in this file
documents the transect point locations and applies to dust and soil samples as well.
Column header information follows:
CollectionID—This provides a join field to the "AnalyticResults.csv" table.
Site—The site at which (or near which in the case of the transect samples) the sample was
taken
Date—The date on which the sample was taken
Media—The media sampled. See AnalyticResultsReadme.txt for media types and the
"Methods.txt" file for sample descriptions.
Vegetation—For soil and Dust samples, the type of vegetation at the sample location
Latitude—For transect samples, the latitude of the sample point, referenced to NAD83. The
latitude and longitude attached to the snow samples in this file documents the transect point
locations and applies to dust and soil samples as well.
Longitude—For transect samples, the latitude of the sample point, referenced to NAD83. The
latitude and longitude attached to the snow samples in this file documents the transect point
locations and applies to dust and soil samples as well.
SnowDepth—Depth of the snowpack at the point where the snow sample was taken. This may
be a useful covariate for analysis. The snow sample was a composite of all snow through the
depth of the snowpack, so a thick snowpack may dilute the metals in the snow sample.
COMMENTS—Comments of field personnel during sample collection.
40
Analytic Results
This dataset includes the laboratory analytical information for all samples collected as described
in the "Methods.txt" file and above. For this project, we collected snow, soil, dust, wetland
sediment, water, periphyton, and tadpole tissue. Please contact Birgit Hagedorn
(bhagedorn@uaa.alaska.edu), Margaret Perdue (margaret_perdue@fws.gov), or Mari Reeves
(mari_reeves@fws.gov).
All laboratory methods may be viewed here:
https://www.uaa.alaska.edu/enri/labs/aset_lab/index.cfm
Or more specifically, at the following links:
ICP-MS:
https://www.uaa.alaska.edu/enri/labs/aset_lab/Methods/aset-lab-methods-icp-msanalysis.cfm
Ion Chromatography:
https://www.uaa.alaska.edu/enri/labs/aset_lab/Methods/aset-lab-methods-ionchromatograph.cfm
Carbon in Water:
https://www.uaa.alaska.edu/enri/labs/aset_lab/Methods/methods-carbon-in-water.cfm
Site—The wetland ID. This provides a join field with the “Site.csv” table.
Date—The date on which the sample was collected.
Media—The media sampled. File includes data from the following media: snow, soil, dust,
wetland sediment, water, and tadpole tissue.
DepthGroup—For water samples, whether water collected was at the surface ("shallow") or the
bottom ("deep") of the wetland. NA means that depth grouping was not relevant to this sample
type (e.g. tissue or dust).
SampleType—Describes whether the sample was taken for different quality control reasons,
including a duplicate sample (where two samples of the same type were taken in different
containers in the field) or a split (where the lab took one sample taken in the field and divided it
into two in the lab to test repeatability of laboratory procedures. See links above for further
methods information and also the “Methods.txt” document for further information about field
sampling design.
Location—Some samples were taken on transects points starting from the edge of the road (see
"Methods.txt" file). This column reports the distance in meters from the road toward the
wetland (or on one side of it) where the sample was taken.
Method—For water samples, describes the type of analysis that produced the result, including
dissolved (filtered) or total (unfiltered) metals or anions or organic carbon.
Analyte—We report data for the following elements, anions, and types of organic carbon that
we measured.
41
Abbreviation
Ag
Al
As
Ba
Be
Br
Ca
Cd
Cl
Co
Cr
Cu
DOC
F
Fe
K
Mg
Mn
Mo
Na
Ni
NO3
NO2
Pb
PO4
Sb
Se
Si
SO4
Th
Tl
TOC
U
V
Zn
Full Name
Silver
Aluminum
Arsenic
Barium
Beryllium
Bromide
Calcium
Cadmium
Chloride
Cobalt
Chromium
Copper
Dissolved Organic Carbon
Fluoride
Iron
Potassium
Magnesium
Manganese
Molybdenum
Sodium
Nickel
Nitrate
Nitrite
Lead
Phosphate
Antimony
Selenium
Silicon
Sulfate
Thorium
Thallium
Total Organic Carbon
Uranium
Vanadium
Zinc
Value—The numerical value for the analyte in that row. If an analyte was reported as below the
analytical limit of detection, then no value is given.
42
Units—The units of measure for the analyte value. These are mg of the analyte per kg of the
media (mg/kg) for solid media. All solid media samples are reported as mg/kg wet weight for
tissue and dry weight for all other solid media (soil, dust, sediment). All water and snow
elemental concentrations are given in micrograms of analyte per L of water (or melted snow).
All anions measured in the laboratory are given in mg/L. Organic Carbon is given as mg/kg for
solids or mg/L for liquids.
belowLOD—This is a TRUE/FALSE field describing whether the analyte was below (TRUE) or
above (FALSE) the limit of detection “LOD”
LOD—The analytical limit of detection for that analyte in that sample. These varied by sample
run or sometimes even by sample, when weights were low.
Flag—TRUE describes whether concerns were raised about the quality of the sample or lab
analysis - FALSE means no concerns were raised, however dataset has not undergone rigorous
QC due to budget cuts and has not been analyzed rigorously, at which point some errors
become apparent.
Comment—Describes why the sample was flagged.
CollectionID—The label assigned each sample in the field and a join field to the table,
“AnalyticAttributes.csv”. Samples were given unique labels that did not reveal site name or
other information, so that analyses were ”blind” by the lab.
Temperature logger data
Temperature.csv
Temperature loggers were programmed to record each hour. Recorded values that were
suspect based on field notes are “flagged” and the issue and resolution provided by field
personnel are recorded in the data file with the flagged values.
The "Temperature.csv" file contains information from continuous data loggers deployed at each
site for the summer months during 2010-2012. We measured temperature at hourly intervals
with HOBO tidbit data loggers (Onset Computer Corporation, Cape Cod, MA, USA,
http://www.onsetcomp.com/products/hobo-data-loggers/waterproof) between late April
(when data loggers were deployed) and mid-September (when loggers were collected).
The location for the temperature loggers at each site was the edge nearest to the road. The
distance from this near edge to the road was determined with a laser range finder or GPS track.
Site—This field provides a join field to the "SiteLocations.csv" file.
Date—The date the temperature was recorded.
Time—The time the temperature was recorded.
Temp—The temperature value in degrees C.
43
LoggerType—Some temperature readings were taken with the conductivity loggers instead of
Hobo loggers - this field describes which logger type recorded the measurement. The
conductivity loggers, water level loggers and tipping bucket rain gauges, six of each type,
deployed at the 2010 pilot sites were co-deployed at the following six sites in 2011/2012: 90,
97, 1024, 1034, 1069, and 1090. The conductivity logger was suspended in a perforated PVC
pipe to protect it while still allowing water movement around the device. The water level logger
was suspended in a stilling well, constructed with PVC pipe. These loggers were deployed at a
slightly different position in the wetland, so data should be interpreted with caution from these
sites.
LoggerPosit—At all sites one logger was deployed at a depth of 10 cm below the water surface.
Deeper sites (>1m depth) had a second logger deployed at a depth of 10 cm above the bottom
of the wetland. The temperature loggers were floated below the water surface, suspended
beneath a piece of foam floating to maintain a consistent logger depth.
Flag—This field is marked TRUE if field personnel recorded an issue with the logger during the
time this measurement was taken.
Issue—When a measurement was flagged, this field records the issue raised by field personnel.
Resolution—When a measurement was flagged, this field records how the issue was resolved
by field personnel.
These temperature data are complex, for example Figure 9 shows a boxplot of all temperature
loggers, showing obvious difference in mean values and the range of temperatures at different
sites. Yet, the detailed time series of the temperatures recorded across sites shows some
striking differences in hydrology and degree of mixing between deep (blue) and shallow (green)
water within sites. These types of patterns in variation will be important to consider when
analyzing the temperature data.
Conductivity Logger Data
Conductivity.csv
The “Conductivity.csv” file contains data from 6 continuous conductivity data loggers that were
deployed at 6 of the study sites. The conductivity loggers, water level loggers and tipping
bucket rain gauges, six of each type, deployed at the 2010 pilot sites were co-deployed at the
following six sites in 2011/2012: 90, 97, 1024, 1034, 1069, and 1090.
Column information follows:
Site—The site at which the logger was deployed. This is a join field to the “SiteLocations.csv”
file.
44
Figure 9. Patterns in Temperature Data. Top panel shows box and whisker plots of
temperature data at deep (D) and shallow (S) loggers at each site. Bottom three panels show
variation in temperature data within and across sites. Blue series are deep loggers and green
series are shallow loggers; 2011 and 2012 data are presented for each site to show variation
with time as well as with depth. Panels suggest different amounts of mixing between deep
and shallow waters within sites, suggesting different hydrology.
DateTime—Loggers were set to log hourly for the summer months from 2010 through 2012
(they were pulled for winter and redeployed each year). This column records the date and time
of the recording.
Val (us/cm)—The value recorded in micro Siemens per centimeter.
45
Road data
RoadsInfo.csv
The distance from each site to the nearest road was measured in the field with a laser range
finder where shorter distances and lack of obscuring vegetation allowed this. For sites further
away from the road and out of view of it, a latitude and longitude were marked and a distance
determined using GIS. The road traffic data are available for most roads included in the study
from the State of Alaska Department of Transportation (AKDOT). Traffic data are available at:
http://www.dot.state.ak.us/stwdplng/mapping/adt.shtml
The “RoadsInfo.csv” file provides measures of distance from each wetland site to the nearest
road and whether that road was paved or gravel, as this was a part of the study design. Further
information may be found in the “Methods.txt” file.
Column Information follows:
Site—This provides a join field for other information in this dataset.
ROADDISTANCE
—This is the measured distance in meters from the water’s edge to the
road.
RoadType—Describes whether the road was paved or unpaved (gravel). Gravel roads may be
coated with dust suppressants or oil for dust control.
Habitat
Habitat.csv
The habitat.csv data set includes habitat data that were collected for this effort.
Habitat parameters were sampled and wetland depth, area and volume were measured in the
field as planned in 2011 and 2012 and the data are included with this data submission. Habitat
observations were made based on USEPA protocols (2007). Habitat observations were made at
10 equally spaced littoral/riparian locations around each wetland. Littoral plots were 15 meters
wide, extending 10 meters into the wetted wetland, and riparian plots were 15 x 15 meters.
The data have undergone little analysis due to cuts in the federal budget.
Column Information
site_id—The identifier for each wetland in which invertebrates were sampled. Each “site_id”
follows the following naming convention: Three-letter refuge code, two to four digit individual
identifier for each site.
coll_date—The date habitat information was collected from each wetland.
station—Habitat observations were made based on USEPA protocols (2007). Ten equally
spaced littoral and riparian plots were established around the perimeter of each wetland. The
10 stations were labelled A-J.
parameter—Habitat observations were made on a number of parameters explained in USEPA
46
(2007)
littoral_depth—depth (x.x m) of wetland one meter from wetted margin
surface_film—N=None; S=Scum; A=Algal Mat; O=Oily; OT=Other
bottom_sub_inorg—Littoral zone inorganic bottom substrate, sand sized or larger, where
0=None; 1=Sparse (<10%); 2=Moderate (10-40%); 3=Heavy (40-75%); 4=Very Heavy (>75%)
bottom_sub_org— Littoral zone organic bottom substrate, where 0=None; 1=Sparse
(<10%);
2=Moderate (10-40%); 3=Heavy (40-75%); 4=Very Heavy (>75%)
bottom_sub_wood— Littoral zone woody bottom substrate, where 0=None; 1=Sparse
(<10%);
2=Moderate (10-40%); 3=Heavy (40-75%); 4=Very Heavy (>75%)
bottom_sub_det— Littoral zone silt, clay or muck substrate, where 0=None; 1=Sparse
(<10%);
2=Moderate (10-40%); 3=Heavy (40-75%); 4=Very Heavy (>75%)
bottom_odor—N=None; H=H2S; A=Anoxic; O=Oil; C=Chemical; OT=Other
macro_submerg—Submergent aquatic macrophytes in littoral zone, where 0=None;
1=Sparse
(<10%); 2=Moderate (10-40%); 3=Heavy (40-75%); 4=Very Heavy (>75%)
macro_emerg—Emergent aquatic macrophytes in littoral zone, where 0=None; 1=Sparse
(<10%); 2=Moderate (10-40%); 3=Heavy (40-75%); 4=Very Heavy (>75%)
macro_total—Total aquatic macrophyte cover in littoral zone, where 0=None; 1=Sparse
(<10%); 2=Moderate (10-40%); 3=Heavy (40-75%); 4=Very Heavy (>75%)
littoral_veg—Total aquatic and inundated herbaceous vegetation in the littoral zone, where
0=None; 1=Sparse (<10%); 2=Moderate (10-40%); 3=Heavy (40-75%); 4=Very Heavy (>75%)
littoral_large_wood—Large wood (>0.3m diameter) in littoral zone, where 0=None;
1=Sparse
(<10%); 2=Moderate (10-40%); 3=Heavy (40-75%); 4=Very Heavy (>75%)
littoral_small_wood—Small wood (<0.3m diameter) in littoral zone, where 0=None;
1=Sparse
(<10%); 2=Moderate (10-40%); 3=Heavy (40-75%); 4=Very Heavy (>75%)
littoral_live_tree—Live trees in the littoral zone, where 0=None; 1=Sparse (<10%);
2=Moderate (10-40%); 3=Heavy (40-75%); 4=Very Heavy (>75%)
littoral_overhang_veg—Vegetation overhanging the littoral zone within 1 meter of the
surface,
where 0=None; 1=Sparse (<10%); 2=Moderate (10-40%); 3=Heavy (40-75%); 4=Very Heavy
(>75%)
littoral_ledges—Ledges or steep drop-offs in the littoral zone, where 0=None; 1=Sparse
(<10%); 2=Moderate (10-40%); 3=Heavy (40-75%); 4=Very Heavy (>75%)
littoral_boulders—Boulders in the littoral zone, where 0=None; 1=Sparse (<10%);
47
2=Moderate (10-40%); 3=Heavy (40-75%); 4=Very Heavy (>75%)
littoral_human_struct—Human-made structures in the littoral zone, where 0=None;
1=Sparse
(<10%); 2=Moderate (10-40%); 3=Heavy (40-75%); 4=Very Heavy (>75%)
rip_canopy_type—Riparian canopy (>5 meters high), where N=None; D=Deciduous;
C=Coniferous; M=Mixed
rip_canopy—Riparian canopy (>5 meters high), where 0=None; 1=Sparse (<10%);
2=Moderate (10-40%); 3=Heavy (40-75%); 4=Very Heavy (>75%)
rip_underst_type—Riparian understory (0.5 to 5 meters) cover, where N=None;
D=Deciduous;
C=Coniferous; M=Mixed
rip_underst_wood—Woody riparian understory (0.5 to 5 meters) cover, where 0=None;
1=Sparse (<10%); 2=Moderate (10-40%); 3=Heavy (40-75%); 4=Very Heavy (>75%)
rip_underst_herb—Herbaceous riparian understory (0.5 to 5 meters) cover, where 0=None;
1=Sparse (<10%); 2=Moderate (10-40%); 3=Heavy (40-75%); 4=Very Heavy (>75%)
rip_grcover_wood—Woody riparian ground cover, where 0=None; 1=Sparse (<10%);
2=Moderate (10-40%); 3=Heavy (40-75%); 4=Very Heavy (>75%)
rip_grcover_herb—Herbaceous riparian ground cover, where 0=None; 1=Sparse (<10%);
2=Moderate (10-40%); 3=Heavy (40-75%); 4=Very Heavy (>75%)
rip_grcover_water—Inundated riparian ground cover, where 0=None; 1=Sparse (<10%);
2=Moderate (10-40%); 3=Heavy (40-75%); 4=Very Heavy (>75%)
rip_grcover_bare—Barren or bare dirt riparian ground cover, where 0=None; 1=Sparse
(<10%);
2=Moderate (10-40%); 3=Heavy (40-75%); 4=Very Heavy (>75%)
shoreline_inorg—Riparian inorganic substrate (sand or larger) within 1 meter of wetted
edge,
where 0=None; 1=Sparse (<10%); 2=Moderate (10-40%); 3=Heavy (40-75%); 4=Very Heavy
(>75%)
shoreline_wood—Riparian woody substrate within 1 meter of wetted edge, where
0=None;
1=Sparse (<10%); 2=Moderate (10-40%); 3=Heavy (40-75%); 4=Very Heavy (>75%)
shoreline_detrit—Riparian silt, muck, or detrital substrate within 1 meter of wetted edge,
where
0=None; 1=Sparse (<10%); 2=Moderate (10-40%); 3=Heavy (40-75%); 4=Very Heavy (>75%)
shoreline_veg—Riparian vegetation substrate within 1 meter of wetted edge, where
0=None;
1=Sparse (<10%); 2=Moderate (10-40%); 3=Heavy (40-75%); 4=Very Heavy (>75%)
buildings—Buildings visible from riparian zone, where 0=None; P=Present outside of
riparian
48
plot; C=Present within riparian plot
commercial—Commercial structures visible from riparian zone, where 0=None; P=Present
outside of riparian plot; C=Present within riparian plot
trash—Trash visible from riparian zone, where 0=None; P=Present outside of riparian
plot; C=Present within riparian plot
roads—Roads visible from riparian zone, where 0=None; P=Present outside of riparian
plot; C=Present within riparian plot
powerlines—Powerlines visible from riparian zone, where 0=None; P=Present outside of
riparian
plot; C=Present within riparian plot
value—explained above
comments—comments
Wetland Depth and Volume
WetlandDepthVolume.csv
The WetlandDepthVolume.csv data set includes wetland depth and area data that were
collected for this effort.
Site_id—The identifier for each wetland in which invertebrates were sampled. Each “site_id”
follows the following naming convention: Three-letter refuge code, two to four digit individual
identifier for each site.
coll_date—The date habitat observations and depth measurements were collected from each
wetland
area—the area of the wetland (m2), as measured by walking the perimeter with a Garmin etrex
legend GPS
mean_depth—the mean of 18 measured depths (3 transects, with 6 equally spaced depth
measurements) (x.x m)
volume— volume of each wetland using the measured area * mean depth (m3)
mean_lit_depth—mean littoral depth calculated from the 10 littoral depth measurements (see
habitat.csv data and read-me files) (x.x m)
max_depth—Maximum measured depth (x.x m) in wetland.
frogs—Frogs observed at time of habitat measurements, where no=no frogs observed, and
yes=frogs observed
floating_mat—Presence of floating vegetation mat along at least 25% of the perimeter, where
no=no floating mat, and yes=floating mat present
comments—comments
49
Invertebrate Community Data
Invert.csv
The Invert.csv data set includes information on the invertebrate community that was collected
for this effort. Invertebrates were collected based on USEPA protocols (2007). Invertebrates
were collected at 10 equally spaced littoral locations around each wetland. Invertebrates were
collected with a 500 µm D-frame net, with a one meter linear sweep through each of the 10
littoral locations, and composited into a single sample. All samples were preserved in the field
with 70% ethanol. Lab processing and enumeration followed USEPA protocols (2006). Each
macroinvertebrate composite sample was subsampled to a fixed count of 500 ±20% organisms
(using a Caton subsampler) to standardize the taxonomic effort across all wetlands. In addition,
a 15–30-minute search through the remaining sample to select any large and/or rare taxa that
may have been missed during subsampling and all predator organisms. Using dissecting and
compound microscopes, we identified insects to genus (or lowest practical taxon) except for
midges, which were identified to subfamily or tribe. Non-insects were generally identified to
higher taxa (usually family, order, or higher). Contact Dan Bogan (dlbogan@uaa.alaska.edu) or
Dan Rinella (djrinella@uaa.alaska.edu) at the University of Alaska at Anchorage.
Column Information
site_id—The identifier for each wetland in which invertebrates were sampled. Each “site_id”
follows the following naming convention: Three-letter refuge code, two to four digit individual
identifier for each site.
coll_date—The date invertebrates were collected from each wetland.
position—For sites sampled in 2010, samples were collected “near” the suspected impact
(road), and “far” from the road. In 2011 and 2012, all samples collected at each site were
composited into a single sample.
Taxa—invertebrate taxa enumerated. Primary taxonomic references were Wiggins (1996),
Smith (2001), and Merritt et al. (2008), and Thorpe and Covich (2010).
RelativeDensityCountPerSquareMeter—Numerical values are estimated (semi-quantitative)
densities in organisms per meter squared. A zero in the cell means that the taxa was present in
the sample (found in the predator pick of the entire sample), but was not found in the
subsample, so no accurate density estimates could be made.
A blank cell indicates that that the taxa was not observed in the sample or the predator pick
(see "Methods.txt" and citations below). Numbers in the table represent a semi-quantitative
estimate of invertebrate abundances (organisms/m2). Numbers were derived using the
following formula: No. of orgs found * (30/no. level 1 grids picked * 30/ level 2 grids
picked)/3.3.
50
Disease Data
Batrachochytrium dendrobatidis (Bd)
We sampled both site water and opportunistically-encountered adult frogs for the fungal
pathogen Batrachochytrium dendrobatidis (Bd) during all three field seasons. Only the water
data are included with this submission. For information about the adult animals, please contact
Tara_Chestnut@usgs.gov.
Bd in Water
BDFilterResults.csv
BDFilterResults.csv data set includes information on 222 water filters that were analyzed for Bd
(Batrachochytrium dendrobatidis), a chytrid fungus that can cause chytridiomycosis in
amphibians. Samples were analyzed by the U.S. Geological Survey using methods similar to
those described in Kirshtein et al. 2007. The data have not yet been analyzed in conjunction
with the water quality and water chemistry or roads data. They have also not been corrected
for sampling effort, which differed across years. Our partners at USGS plan to lead this analysis
and publication of these data. Please contact William_battaglin@usgs.gov or
Tara_chestnut@usgs.gov with questions about these data.
site_id—The identifier for each wetland in which an animal was sampled. Each “site_id” follows
the following naming convention: Three-letter refuge code, two digit or longer individual
identifier for each site within a refuge. This column provides a join field to the
“SiteLocations.csv” file, which includes Latitude and Longitude for the file and to the
“RoadInfo.csv” file which includes distance to the nearest road and whether it is paved or
gravel, and to the “Habiatat.csv” and “DepthVolume.csv” files, which describe other attributes
regarding the site.
Date—The date of sample collection
WQ_ID—This is a link field to the “WaterQualityBDFilters.csv” data sheet. The WQ_ID always
follows the same format(site_id_”“date_””chytridfilter#’#’”) For example,
KNA17_070710_chytridfilter#1.
Filter_number—This is the filter number from a set of filters collected at a given site on a given
date. Typically each “sample” consisted of 3 filters, but in some cases less than 3 filters were
collected or analyzed. Values combine with the site_id and date fields to create a unique
WQ_ID identifier for each filter.
average zoospores /L—This is the average concentration of Bd zoospores per liter of water. The
value is calculated from two replicate analysis that were performed on each filter. A value of 1
51
it given when the result reported by the laboratory was “positive but not quantitative”
(Kirshtein et al. 2007).
Water Quality Measurements Concurrent with Bd in Water Sampling
WaterQualityBdFilters.csv
“WaterQualityBDFilters.csv” – This table describes water quality information collected with a
YSI sonde concurrent with sampling for Batrachochytrium dendrobatidis in water. See
“Methods.txt” file for more information on field data collection methodology.
Column information follows:
Site—This is a join field to the “SiteLocations.csv” file which includes the latitude and longitude
of the site.
Date—The date on which monitoring occurred.
Filter—Three filters were taken for each sample. This provides the filter number.
WQ_ID—This column concatenates site date and filter number to provide a unique identifier
for the record. This column provides a join field for the BDFilterResults.csv column. The
concatenated site and date can provide a join field for the "SiteEvents.csv" file, which
documents weather, site width and length.
The WQ_ID always follows the same format(site_id_”“date_””chytridfilter#’#’”) For example
KNA17_070710_chytridfilter#1.
Temp_C—The temperature of the water in Celsius.
Spc—Specific conductance of the water in uS/cm.
Depth_m_reading—The depth of the sample in meters, as measured by the instrument. Water
quality measurements were taken on depth profiles at 10 cm (surface) and subsequently every
30 cm until the sonde reached the bottom of the wetland.
pH—The pH of the sample, i.e. the negative log of the hydrogen ion concentration.
Cl_ppm—The chloride ion concentration in parts per million, as measured by the sonde.
Additional laboratory-based chloride ion concentrations are available when water samples
were taken in the “AnalyticResults.csv” file.
Turbidity_NTU—Turbidity from a calibrated nephelometer measured as the propensity to
scatter light and reported in calibrated Nephelometric Turbidity Units (NTU).
DO_perc—The dissolved oxygen concentration in the water, reported as a percentage.
DO_ppm—The dissolved oxygen concentration in the water, reported as parts per million or mg
per liter.
Conductivity— Conductivity measures the ability of a material to conduct electrical current,
reported in us/cm.
Salinity_ppt—The measure of salinity of the water sample in parts per thousand provided by
the water quality sonde.
52
Perkinsus Like Organism
We have observed a disease organism of conservation concern at sites on the Kenai Refuge for
at least a decade. It has wiped out entire cohorts of tadpoles from some breeding sites in some
years. We have never been funded explicitly to examine this organism, but it shows some
interesting correlations with site temperature and water quality, including pH, in preliminary
analyses. Further study of this organism is warranted. Information about observation of this
disease in metamorphic wood frogs from the Kenai National Wildlife Refuge is included in the
FrogAbnormalities.csv file. For more information, please contact mari_reeves@fws.gov or
margaret_perdue@fws.gov.
Anchorage Study
Descriptions of the Anchorage Water Quality Study and Quality Control Information
ANC_WQ_Study_2009_.xlsx
AnchorageRawSedWaterResultsMethodsQC.pdf
Anchorage Water Quality Study 2009
The ANC_WQ_Study_2009_.xlsx file contains summary information from a pilot-scale study
that was conducted in the fall of 2009 to inform the sampling design of the Kenai study that is
the focus of this data submission. The AnchorageRawSedWaterResultsMethodsQC.pdf provides
information from the analytical lab regarding specifics of metal analysis methods, quality
control information, and raw results in an adobe format. These results are shared in a .csv
format as well and those datasets are described below.
Pilot Study Title: Storm Water Runoff: What effects do best management practices (BMPs) and
storm events have on water quality in wetlands in the Anchorage Bowl?
Conducted by: Environmental Contaminants Program, Anchorage Fish & Wildlife Field Office
Contact persons: Meg Perdue, e-mail: margaret_perdue@fws.gov
Mari Reeves, e-mail: mari_reeves@fws.gov
Background and Justification
Historically the Municipality of Anchorage (MOA) has managed storm water by routing it
directly into the area’s streams. This strategy has come under scrutiny with the recognition of
adverse impacts to the hydrology and water quality of several streams to the point that several
are now listed by the Alaska Department of Environmental Conservation as impaired water
bodies. To address this issue the MOA has developed guidance and begun re-routing storm
water runoff through natural wetlands and constructed systems, collectively referred to as best
53
management practices (BMPs). However, limited assessment of the efficacy of these BMPs has
been conducted to date. There is also a need for region specific data on the effects of storm
events on contaminants loading to natural and constructed wetlands. Pollutant wash-off into
receiving waters from roads and other impervious surfaces during storm events and through
snow melt is recognized as an important issue affecting water quality and biotic communities.
Study Design
From a candidate list of 51 prospective sites in the Anchorage Bowl, which were
stratified by wetland type (“constructed wetland receiving runoff”, “nonconstructed wetland
receiving road runoff”, “reference wetland receiving no runoff”), then chosen by ranking the
sites randomly within these three categories for selection (ProspectiveSites tab) yielded 17
study sites assigned to three treatment categories, seven constructed sites including
constructed wetlands and sedimentation ponds, seven natural wetlands where storm water
runoff is directly routed through drainage pipes and canals and three reference sites isolated
from roads or other development . At each site, water quality measurements including
temperature, pH, dissolved oxygen, conductivity and turbidity and site characterization data
describing aquatic cover, adjacent upland land cover, wildlife observations and human
influences were collected at the 4 polar coordinates of each wetlands (N,S, E, W) and at the
inlet and the outlet (where there was one) to assess spatial variability. Water quality data was
collected once in September and once in October of 2009, to assess temporal variability, and
water and sediment samples were collected once to assess contaminant levels for EPA priority
metal pollutants. Data were summarized to assess whether water quality improved from the
inlet to the outlet at constructed sites, what influence storm water drainage had on water
quality at natural sites.
Information on different worksheet tabs are described below.
Prospective_Sites—Provides a list of all sites considered for inclusion in the study. In some
cases safety or other issues prevented us from sampling them.
MOA Map—This field lists the map number on which the site is show from the
Municipality of Anchorage’s Wetland Atlas:
http://anchoragestormwater.com/wetlands2008.html
Rank—Shows a number randomly assigned to each site within each strata. The rank
determined the search order for sites to be added to the study. Sites were added in this order
unless there was inadequate water or safety or other reasons prevented site inclusion.
Comments—Describes site observations, including those that precluded sites from
study.
Sample_Coord—This worksheet details the exact GPS locations of the cardinal directions of the
site, and the inlet and outlet locations, in addition to dates sampled.
54
Dropdown Cells—Describes fields on which information was gathered during the different
water quality sampling events.
WQ_Data Key—Describes the data collected during the water quality monitoring events.
WQ_Data_1 and WQ_Data_2—Record water quality and habitat monitoring data collected in
September and October 2009.
Sediment_Analysis and Water Analysis—provide summary information of metals data by site
type, in comparison to recommended threshold concentrations. The raw sediment and raw
water data are provided as AnchorageSediment.csv and AnchorageWater.csv files, included
with this data submission.
SedimentationPondProgressions and Constructed Wetland Progressions—These worksheets
show preliminary graphics illustrating changes in total metals concentrations in water from the
Inlet to the Outlet of these sites.
Anchorage Sample Log
AnchorageSampleLog.csv
The AnchorageSampleLog.csv file contains Information about the sites sampled for the
Anchorage Water Quality Pilot Study and provides a join field to the “SiteLocations.csv” file,
which provides the latitude and longitude for each site included in this data submission.
SiteName—Descriptive name of the site.
MOAMapNumber—Provides the location of the site on the Municipality of Anchorage Wetland
Atlas, available at: http://anchoragestormwater.com/wetlands2008.html
SiteLocation—This column provides a join field to the SiteLocations.csv file.
NumberSedSamples—The number of sediment samples collected at each site.
NumberWaterSamples—The number of water samples collected at each site. The study was
designed to examine the effects on water quality of routing storm water into natural and
constructed wetlands in the municipality of Anchorage. It was also designed to examine the
efficacy of the constructed wetlands at improving water quality prior to discharge into local
creeks. Not all wetlands had an inlet or outlet. In cases where they did, both were sampled. A
few wetlands had two inlets. In these cases, both were sampled. All sample locations were
measured with a handheld GPS unit. The links to the exact sample locations are contained in
the “ANC_WQ_STUDY_2009.xlsx” worksheet on the “Sample_Coord” worksheet.
WaterSampleLocations—A description of where the water sample locations.
DateSampled—The date the samples were collected.
SedimentID—The sediment sample ID. This provides a join field for the
“AnchorageSediments.csv” file, which contains the lab results.
55
WaterComposite—A sample that composited water from the 4 cardinal directions (N,S,E,W)
was taken at each site. This field shows the sample ID for each of these samples.
WaterInlet1—In cases where there was an inlet that routed road runoff into the wetland, this
sample identified the Inlet sample. In some cases, there were two different inlets. In these
cases both were sampled.
WaterInlet2—In cases where there was an inlet that routed road runoff into the wetland, this
sample identified the Inlet sample. In some cases, there were two different inlets. In these
cases both were sampled.
WaterOutlet—This sample was taken at the outlet of the wetland or constructed sedimentation
pond to a creek.
SampledBy—Describes who collected the sample. MP is Margaret Perdue
(maragaret_perdue@fws.gov) and MR is Mari Reeves (mari_reeves@fws.gov)
SiteType—Prospective wetlands in the Municipality of Anchorage (MOA) were stratified by
their type prior to random selection. These sites included reference sites with no direct storm
water runoff from roads “reference”, natural wetlands into which the MOA has routed storm
water from road runoff, “non-constructed”, and artificial wetlands or storm water retention
ponds, which were constructed by MOA to contain and treat storm water runoff “constructed”.
Sediment Data
AnchorageSedimentMetals.csv
The “AnchorageSedimentMetals.csv” file provides metals concentrations in sediment for the
Anchorage 2009 study. These are metals data analyzed by ICP/MS and are sediment digest
samples, and are comparable to the sediment samples from the Kenai data set, based on the
methods used. Further information regarding quality control, and analytical lab methodology
can be found in the file: “AnchorageRawSedWaterResultsMethodsQC.pdf”, included with this
data submission.
Column information follows:
SampleID—This provides a join field to information about sample location contained in the
“AnchorageSampleLog.csv” file.
The columns include the Analyte, whether it was the Result or the limit of detection (LOD) and
the sample units. In this datasheet, all results are milligrams per kilogram dry weight (mg/kg
dw).
Analyte abbreviations follow:
Abbreviation
Full Name
Ag
Silver
Al
Aluminum
As
Arsenic
56
B
Ba
Be
Br
Ca
Cd
Cl
Co
Cr
Cu
Fe
K
Mg
Mn
Mo
Na
Ni
Pb
Sb
Se
Si
Sr
Th
Tl
U
V
Zn
Boron
Barium
Beryllium
Bromide
Calcium
Cadmium
Chloride
Cobalt
Chromium
Copper
Iron
Potassium
Magnesium
Manganese
Molybdenum
Sodium
Nickel
Lead
Antimony
Selenium
Silicon
Strontium
Thorium
Thallium
Uranium
Vanadium
Zinc
Water Data
AnchoarageWaterMetals.csv
The “AnchorageWaterMetals.csv” file provides metals concentrations in sediment for the
Anchorage 2009 study. These are metals data analyzed by ICP/MS and are unfiltered water
samples, and are comparable to the samples labeled as sample type, “total” from the Kenai
data set, based on the methods used. Further information regarding quality control, and
analytical lab methodology can be found in the file:
“AnchorageRawSedWaterResultsMethodsQC.pdf”, included with this data submission.
Column information follows:
57
SampleID—This provides a join field to information about sample location contained in the
“AnchorageSampleLog.csv” file.
The columns include the Analyte, whether it was the Result or the limit of detection (LOD) and
the sample units.
In this datasheet, all results are milligrams per liter (mg/L, or ppm).
Analyte abbreviations follow:
Abbreviation
Full Name
Ag
Silver
Al
Aluminum
As
Arsenic
B
Boron
Ba
Barium
Be
Beryllium
Br
Bromide
Ca
Calcium
Cd
Cadmium
Cl
Chloride
Co
Cobalt
Cr
Chromium
Cu
Copper
Fe
Iron
K
Potassium
Mg
Magnesium
Mn
Manganese
Mo
Molybdenum
Na
Sodium
Ni
Nickel
Pb
Lead
Sb
Antimony
Se
Selenium
Si
Silicon
Sr
Strontium
Th
Thorium
Tl
Thallium
U
Uranium
V
Vanadium
Zn
Zinc
58
Summary of Controlled Experiments
Controlled Experiment 1:
The first controlled experiment for this project was conducted in 2010. The results showed that
tadpole behavior was significantly affected by low but environmentally relevant levels of
copper (5ppb). The abstract is shown below. The complete results of this experiment are
available in a peer-reviewed article in the on-line journal Ecosphere at:
http://www.esajournals.org/doi/pdf/10.1890/ES11-00046.1
Twice as easy to catch: A toxicant and a predator cue cause additive reductions in larval
amphibian activity.
By, Mari K. Reeves, Margaret Perdue, Gareth D. Blakemore, Daniel J. Rinella, and Marcel
Holyoak
Abstract: The influence of toxicants on predator-prey interactions is complex because toxicants
may influence predators or prey, making it especially difficult to disentangle the multiple
stressor effects on prey of both predators and toxicants. We built on a prior field study in which
we demonstrated that a chemical contaminant, copper (Cu) and odonate predators were
correlated with more frequent observations of skeletal abnormalities in Alaskan wood frog
(Lithobates sylvatica) tadpoles. Our prior study established a multiple stressor effect linked to
an important environmental phenomenon (malformed amphibians) but did not provide clear
mechanisms that might guide management. We here investigated behavioral mechanisms
because of their potential to produce large changes in predation dynamics, and because in
published studies low concentrations of Cu produced behavioral changes in predator-detection
in fish. Surprisingly, low but environmentally relevant concentrations of Cu (5 µg/L) combined
with chemical cues from a predator (Aeshna sitchensis) to produce large changes in the
behavior of larval amphibians. Experiments demonstrated that a low concentration of Cu did
not inhibit the ability of wood frog tadpoles to detect chemical cues of predators by olfactory
means, but produced strong behavioral effects, causing tadpoles to reduce activity and alter
microhabitat use. These results occurred with Cu at an exposure level lower than any we could
find reported as toxic to amphibians in the literature. When Cu and predator cues were
administered together, the activity reduction was additive and stronger at earlier life stages.
We suggest that Cu intoxication would be disadvantageous to larval amphibian prey with
prolonged exposure to predators during development, and we present field data from 2010
that support this assertion. Our study demonstrates the need to use sensitive behavioral
bioassays and to investigate multiple stressor mechanisms to understand how multiple threats
combine to affect organisms in nature.
59
Controlled Experiment 2:
A second experiment was conducted during the summer of 2012 to determine whether the
additional stress of higher temperatures in conjunction with neurotoxic metals would make
tadpoles even more vulnerable to predation injury and subsequent developmental
malformation via a mechanism of impaired predator avoidance behavior. This second
experiment tested the effects of temperature and even lower, environmentally relevant levels
of copper (1.85 ppb) on predator prey interactions and tadpole behavior. We hypothesized that
tadpoles exposed to copper and higher temperatures would experience the greatest behavioral
changes and increased susceptibility to predator attack. This experiment has been summarized
as a draft manuscript for submission to a peer reviewed journal for publication. We have pasted
the abstract below and included the draft manuscript as Appendix B.
Copper and temperature modulate predator-prey interactions between larval dragonflies and
anurans.
By, Tess Hayden, Mari K. Reeves, Margaret Perdue, Marcel Holyoak, Amanda King, and Carl
Tobin
Abstract:
Amphibians are important indicators of environmental health, and their populations are in worldwide
decline. The causes of these declines are diverse and not well understood. In some cases multiple
stressors and complex causal mechanisms have been identified. Experimental studies have shown that
contaminants can cause the failure of Lithobates sylvaticus tadpoles to initiate predator avoidance
behaviors, potentially leading to increased tadpole capture and injury. Copper is a contaminant known
to negatively affect amphibians and other aquatic organisms at sub-lethal levels. Vehicle exhaust, brake
pad dust, and mining waste are sources of copper, which can enter hydrologic systems through runoff.
Additionally, temperature is known to influence predator-prey interactions of ectotherms and is
predicted to rise as climate changes in some areas. We examined how copper and temperature affect
behavior and predation dynamics between an odonate predator (Aeshna sitchensis) and larval L.
sylvaticus prey. We found that sublethal concentrations of copper near the analytical detection limits for
this element (1.85 µg Cu/L) significantly reduced tadpole and odonate activity. Above-average
temperatures (22°C) significantly increased tadpole activity and decreased dragonfly activity, compared
with ambient-temperature treatments (17°C). These behavioral responses culminated in an increase in
dragonfly attack frequency in the elevated-temperature, copper-exposed treatments. We suggest that
increased concentrations of dissolved copper and elevated water temperatures are harmful to
amphibian prey through maladaptive behavioral responses in the presence of predators.
60
V. DISCUSSION
One published paper and this data publication have already emerged from this study. The
results of a second experiment have been submitted to a peer reviewed journal for publication,
and are currently being revised after reviewer comments for resubmission. Due to cuts in the
federal budget, we did not have adequate funds to fully analyze the field data, which are shared
with this data publication. We propose the following analyses be conducted and reported upon
in the peer reviewed literature.
Paper 1: Effect of roads and climate on water quality, habitat, and wetland community
composition across an urbanization gradient in South-central Alaska
The field data require further analysis to test the following hypotheses prior to reporting and
publication.
1. Roads contribute metals to wetlands with snow melt and rainwater runoff, groundwater
contamination due to percolation through the roadbed, and dust.
2. Metal inputs will differ in magnitude based on road type, wetland-road proximity, and
traffic volume.
3. Roads are associated with higher temperatures in adjacent wetlands—temperature
increases should be stronger with paved roads and shallower wetlands closer to the road.
4. Higher water temperatures lead to increased dissolved metal concentrations in wetlands.
5. Contaminant inputs and temperatures exhibit spatial autocorrelation and spatial and
temporal variability.
6. Macroinvertebrate diversity and abundance will differ with wetland type, road type, and
degree of metals contamination.
7. Wetland habitats will be differentially resistant to change when confronted with
contaminants and changes in climate like shorter hydroperiod and increased temperature.
8. Wetland habitat, contaminants signature, and temperature will jointly control the diversity
and abundance of the invertebrate communities.
Paper 2: Do contaminants, climate change, and habitat characteristics influence amphibian
abnormalities in the wild?
Here, we will take the hypotheses we developed through experimental work in Papers 1 and 2,
and test them using field data. We plan to use spatially implicit and explicit statistical models to
test the following hypotheses:
1. Road-based metal contamination alters the incidence of amphibian abnormalities in nonlinear ways through increased predation driven by changes in community composition,
tadpole intoxication, and alteration of normal predator avoidance behavior.
61
2. Elevated water temperatures facilitate amphibian abnormalities by altering the predation
dynamics between invertebrates and tadpoles to favor the invertebrate predators in these
systems.
VI. MANAGEMENT RECOMMENDATIONS
We submit the following management recommendations based on this project.
Analyze the field data to assess the effects of roads on toxic metal transport to adjacent
habitats. The field data should be analyzed as recommended in the discussion section above,
following the strategies outlined in the original proposal. This study was well designed and the
data were immaculately collected. The data warrant analysis to provide important information
regarding the effects of road development in remote refuges of Alaska and can inform the
National Environmental Policy Act (NEPA) process for road development projects around the
state.
Analyze the field data to test the mechanisms proposed regarding interactions between toxic
metals, predator and prey behavior and increased temperatures (Reeves et al. 2011 and
Hayden et al. in prep). These experimental data showed compelling effects of very low levels of
toxic metals, many of which exceed toxic thresholds at Kenai Peninsula study sites. Naturally
soft water in these lentic wetlands in South-central Alaska likely exacerbates these toxic effects.
Determine population status and trends for wood frogs in South-central Alaska. The
population status of amphibians was not a focus of this study. Nevertheless, the population
status of wood frogs in high abnormality areas like the Kenai Refuge warrants further attention.
The results of a 10-year study of abnormal amphibians at the national scale showed the high
frequencies of abnormal frogs to be uncommon on National Wildlife Refuges (Reeves et al.
2013). This national study highlighted the Kenai and Tetlin National Wildlife Refuges as
abnormality hotspots with unusually high frequencies of abnormal frogs (Reeves et al. 2013). A
conservation genetics study conducted as part of Margaret Perdue’s Master’s Thesis at Alaska
Pacific University suggested an interesting correlation between sites with high abnormality
frequencies and population dynamics, suggesting these high abnormality sites may also be
population sinks on the landscape (see proposal for more information). Population status and
trends of amphibians in Alaska and elsewhere are woefully understudied relative to other taxa
(e.g. birds), even though amphibians play incredibly important roles in ecosystems as predators
and prey, and are declining at precipitous rates globally due to habitat loss, disease,
contaminants, and other stressors. Funding must be directed toward amphibian conservation
and research if we are to stave off the current population declines.
62
The incidence and drivers of certain diseases found during this study warrant further
attention. The disease status of wood frogs was not a focus of this study, however we have
documented diseases through value-added collaborations with our colleagues at the USGS.
Both the incidence of Bd (the Chytrid Fungus) and this relatively poorly described, yet virulent,
protozoan pathogen (the Perkinsus Like Organism) warrant further investigation.
63
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