Owens Lake Habitat Suitability

Models: Validation and Refinements

Final Report to the

Los Angeles Department of Water and Power

29 July 2016

L. Jay Roberts, Ryan D. Burnett, W. David Shuford, and Gary W. Page

Acknowledgements

We thank the staff at LADWP, including Jeff Nordin, Sarah Bryson, Debbie House, and

Collette Zemitis for their guidance and assistance in completing this project. We also

thank Dr. Dave Herbst for providing his valuable expertise in saline-adapted aquatic

invertebrates. The Owens Lake Habitat Working Group has persisted over many years

to see the lake managed to promote bird habitat and we are happy to be part of that

process.

Suggested Citation:

Roberts, L. J., R. D. Burnett, W. D. Shuford, and G. W. Page. 2016. Owens Lake Habitat

Suitability Model Validation and Refinements. Final Report to the Los Angeles

Department of Water and Power. Contribution No. 2076 of Point Blue Conservation

Science, Petaluma, CA.

Point Blue Conservation Science – Point Blue’s 140 staff and seasonal scientists

conserve birds, other wildlife and their ecosystems through scientific research and

outreach. At the core of our work is ecosystem science, studying birds and other

indicators of nature’s health. Visit Point Blue on the web www.pointblue.org.

Cover photo credits (All acquired from Wikipedia, 4/17/2016) in order clockwise from top left:

American Avocet, by Ingrid Taylar, https://commons.wikimedia.org/w/index.php?curid=7013465; Ruddy Duck, by Dick Daniels, https://commons.wikimedia.org/w/index.php?curid=11177905; Snowy Plover, by USFWS Southeast Region, https://commons.wikimedia.org/w/index.php?curid=29813298; Owens Lake aerial photo, by ISS Expedition 28 crew, https://commons.wikimedia.org/w/index.php?curid=16446790; Eared Grebe, by Frank Schulenburg, https://commons.wikimedia.org/w/index.php?curid=29519364; Northern Shoveler, by Tsrawal, https://commons.wikimedia.org/w/index.php?curid=24632300..

Contents

Executive summary ......................................................................................................................... 1

Recommendations ........................................................................................................................... 2

Section 1: Introduction .................................................................................................................... 4

Section 2: Owens Lake ecology, habitat and bird monitoring ........................................................ 8

Invertebrate food sources ............................................................................................................ 8

Key waterbird species ................................................................................................................. 8

Owens Lake habitat and bird monitoring.................................................................................. 11

Description of guilds: Breeding Waterfowl (BWF) .................................................................. 13

Description of guilds: Migrating Waterfowl (MWF) ............................................................... 14

Description of guilds: Breeding Shorebirds (BSB) .................................................................. 16

Snowy Plover ........................................................................................................................ 17

American Avocet .................................................................................................................. 18

Description of guilds: Migrating Shorebirds (MSB) ................................................................ 19

Description of guilds: Diving Waterbirds (DWB) .................................................................... 21

Section 3: HSM Validation and Refinement Methods ................................................................. 23

Section 4: Results .......................................................................................................................... 27

Breeding Waterfowl Guild ........................................................................................................ 27

Basic summary metrics and exploratory data analysis ......................................................... 27

BWF HSM refinement .......................................................................................................... 30

Migratory Waterfowl Guild ...................................................................................................... 34

Basic summary metrics and exploratory data analysis ......................................................... 34

MWF HSM refinement ......................................................................................................... 37

Breeding Shorebird Guild ......................................................................................................... 41

Basic summary metrics and exploratory data analysis ......................................................... 41

BSB HSM refinement ........................................................................................................... 43

Migratory Shorebird Guild ....................................................................................................... 48

Basic summary metrics and exploratory data analysis ......................................................... 48

MSB HSM refinement .......................................................................................................... 50

Diving Water Bird Guild .......................................................................................................... 55

Basic summary metrics and exploratory data analysis ......................................................... 55

DWB HSM refinement ......................................................................................................... 57

Section 5: Discussion .................................................................................................................... 61

HSM strategy ............................................................................................................................ 61

Caveats on our evaluations of the HSMs .................................................................................. 64

Conservation value of Owens Lake .......................................................................................... 66

Recommendations ..................................................................................................................... 67

Structured decision making....................................................................................................... 68

Literature Cited ............................................................................................................................. 69

Appendix ....................................................................................................................................... 74

P a g e | 1

Executive summary

Habitat models used in conservation planning can simplify and streamline important

management decisions and we feel that the HSMs developed by LADWP, and refined as

described in this report, will be useful in managing the water use and conservation of wildlife

habitat at Owens Lake into the future. Our evaluations relied heavily on analyses of available

data as well as expert knowledge to arrive at the best possible refinements we could devise for

the HSMs. The fit between HSM-generated habitat values and field survey bird counts is far

from perfect, but such is the nature of ecological data. Some of our results indicate that

additional unmeasured biological, location, or other features could be important to assessing

habitat, especially for the Breeding Waterfowl, Breeding Shorebirds, and Diving Waterbirds

guilds, thus we encourage LADWP to continue exploring ways to incorporate new information

into the HSMS that could improve the assessment and management of waterbird habitat in the

future. Two additional features that would be particularly useful are the behavior of birds

during surveys (foraging vs. loafing, bathing, etc.) and the presence and abundance of

invertebrate food sources.

Owens Lake provides habitat for birds and other wildlife throughout the year, but certain

periods are particularly important. The summer breeding season for shorebirds is a time when

the Snowy Plover, a species of regional conservation concern, is relatively abundant on the lake.

Activities related to irrigating for dust control that also provide habitat for this species should

continue. Given that the Snowy Plover is regionally important, and that Owens Lake likely

provided important breeding habitat for other salt-tolerant shorebirds prior to water diversion,

the Breeding Shorebirds guild should be considered highest priority for habitat management.

Potential redesigns of existing dust control areas should put Breeding Shorebird habitat among

the top goals of such projects. Owens Lake likely also provided important habitat for migrating

waterbirds during spring and fall, thus the Migratory Waterfowl, Migratory Shorebirds, and

Diving Waterbirds guilds should also receive high priority in terms of management focus.

Breeding Waterfowl were likely not abundant at Owens Lake prior to diversion, and the species

that make up this guild are not generally salt-tolerant, thus we feel like this guild should have

the lowest priority for management habitat at Owens Lake.

P a g e | 2

The HSMs provide information useful in assessing various scenarios relating to operations and

redesign of dust control areas. There may be some potential to reduce overall water use while

maintaining habitat value on the lake, however given that the infrastructure was built with only

dust control in mind it may be difficult to achieve several goals simultaneously. Redesign offers

opportunities to build in infrastructure directly useful in managing habitat value while

optimizing the water use. We encourage LADWP to be innovative and experimental with

redesigns while also carefully and thoroughly monitoring the changes and taking steps to

mitigate any negative outcomes. The larger challenge is to collectively manage Owens Lake dust

control areas to provide habitat value that is more than the sum of the parts.

Recommendations

• Increasing water or optimizing water depths and salinity during certain periods of the

year has potential to improve habitat conditions for several guilds. Increasing shallow flooding

in July and August would provide habitat for migrating shorebirds during a time when such

habitat is limited in the region, could increase persistence of water boosting productivity of

invertebrates, and could also benefit the breeding guilds either by extending the breeding

season or providing habitat for juveniles. Efforts to manipulate salinity could improve habitat

suitability for guilds for which high salinity is limiting as well as promote invertebrate

productivity.

• There is still room for improving the utility of HSMs through several avenues, including

improving the scale, timing, and detail of monitoring both birds and habitat upon which the

HSM evaluations were based; understanding the variability of important habitat features within

individual DCAs and how those patterns might affect guild abundance; performing additional

studies to determine how to optimize the combination of seasonal HSVs; and exploring

additional habitat or ecological parameters like invertebrate food sources that might be

important features of habitat value.

P a g e | 3

• LADWP should be encouraged to experiment with redesign of DCAs to reduce water use

while maintaining waterbird habitat. Careful and thorough monitoring should be carried out as

part of the redesign process. Clearly defined mitigation plans should be established prior to

redesigns, and when experimental redesigns do not achieve habitat value goals, then mitigation

should be implemented. Current infrastructure may be inadequate to monitor and manipulate

conditions such as salinity that are fundamental to managing habitat value at many DCAs, and

therefore new tools should be implemented where available that can alter those conditions.

• LADWP and the Owens Lake Habitat Working Group should work together to establish

an adaptive management framework for evaluating implemented and planned activities,

operations, and redesign of DCAs. It may be possible to minimize water use while maintaining

habitat value throughout the lake. This challenging outcome would be more feasible given a

detailed framework for accounting for activities across all DCAs within this complex facility, and

is likely only possible with substantial efforts to improve monitoring of habitat conditions and

birds.

P a g e | 4

Section 1: Introduction

Owens Lake sits at about 1,097 m (3600 ft) elevation at the southern end of the Owens Valley,

Inyo County, at the interface of the Great Basin and Mojave deserts in eastern California. To the

west of the lake rises the steep eastern slope of the Sierra Nevada and to the east the similarly

abrupt flank of the Inyo Mountains. Although set in an arid desert climate cast by the rain

shadow of the Sierra Nevada, Owens Lake was formerly fed by abundant snowmelt from many

Sierran streams that flowed first into the Owens River or into the lake itself. Lying in a terminal

basin with no outlet and high evaporation rates, Owens Lake has been hypersaline for the last

6000 years (Newton 1991 in Jehl 1994).

Reports by early naturalists are few and their observations often fleeting, but their accounts

describe an expansive lake, at times plied by steamboats, and hosting an abundant and diverse

suite of waterfowl, shorebirds, and grebes (Grinnell 1911–1927; Jehl 1994, 1996). The most

numerous species noted were those typical of other alkaline and saline lakes in the Great Basin

(Jehl 1994, Shuford et al. 2002), including the Eared Grebe (Podiceps nigricollis), American

Avocet (Recurvirostra americana), phalaropes (Red-necked [Phalaropus lobatus] and likely

Wilson’s [P. tricolor]), and ducks (most likely dominated by salt-tolerant Northern Shovelers

[Anas clypeata] and Ruddy Ducks [Oxyura jamaicensis]). The great abundance of birds was

indicated by sightings of “ducks and waders, scattered about in large masses … on the beach

and out in the water,” an estimated 1300 avocets per mile, “groups and crowds” of phalaropes,

and an assessment that literally thousands of birds were within sight of one spot (Grinnell

1911–1927). The base of the food web for the abundant birdlife was a salt-tolerant biota of

algae (apparently including the filamentous green alga Ctenocladus circinnatus), salt flies

(Ephedra), and perhaps brine shrimp (Artemia) (Herbst and Prather 2014). Flies were the most

noticeable, clustered in a two-feet-wide band at the high water mark and rising in clouds at

one’s feet (Grinnell 1911–1926, Herbst and Prather 2014).

Although the lake’s level varied with wet and dry climate cycles, agricultural development in the

Owens Valley in the late 1800s resulted in irrigation withdrawals from the Owens River that

substantially limited inflows to Owens Lake (Jehl 1994, Herbst and Prather 2014). In 1913 the

P a g e | 5

City of Los Angeles completed an aqueduct that captured all streams flowing east from the

Sierra as far north as Bishop and cut off 100 km (62 mi) of the Owens River channel above

Owens Lake (Herbst and Prather 2014). By about 1926, the remnant lake consisted of only a

permanent pool of saturated brine in its western portion, where it was once deepest, and, on

the lakebed margins, scattered seeps, springs, and wild wells, and their outflows onto salt flats

and shallow ponds. Although the lake’s demise greatly reduced its ability to support avian life,

the scarce aquatic habitats on the edges of the playa represent habitat refuges and potential

colonization sources for the renewal of an interconnected aquatic ecosystem in the Owens Lake

basin (Herbst and Prather 2014).

With the dewatering of the lake, winds blowing over the dry lakebed created periodic alkali

dust storms that far exceeded national air quality standards and threatened public health. In

1998, the City of Los Angeles and the Great Basin Unified Air Pollution Control District signed a

Memorandum of Agreement that the City of Los Angeles would take actions to mitigate for

particulate air pollution (www.gbuapcd.org/owenslake/index.htm). Environmental compliance

under the dust control project initially allowed three Best Available Control Measures (BACM)

to reduce fugitive dust: shallow flooding (shallow ponds or sheet flow), managed (native)

vegetation, and gravel cover. Starting in 2001, Los Angeles Department of Water and Power

(LADWP) has implemented the Owens Lake Dust Mitigation Program. Built in phases, it now

covers about 117 km2 (45 mi2). When fully implemented, the project will include a total 125.9

km2 (48.6 mi2) of the 285 km2 (110 mi2) of the Owens lakebed, and will use some additional or

modified BACMs, such as tillage (mechanical roughing and developing soil clods), to control

dust (GBUAPCD 2016). Through 2014, about 85% (35.8 mi2/42 mi2) of the dust control consisted

of shallowly flooded areas, which encompassed about a third of the entire lakebed (LADWP

2014a).

Beyond controlling dust, the current large extent of shallow flooding has rejuvenated the

ecosystem by greatly augmenting the available aquatic food resources (Herbst 2003) and

dramatically increasing the diversity and abundance of waterbirds at Owens Lake (LADWP

2014b). Still, the use of this flooded habitat by waterbirds presents long-term management

P a g e | 6

challenges given that LADWP could potentially implement other BACMs that would provide

substantial water savings at Owens Lake. Depending on the extent of use of alternative BACMs,

water savings might be great given LADWP has used an average of 70,000 acre-feet of water

each year to control dust on Owens Lake, representing 20% of its annual export from the

Eastern Sierra (LADWP 2014b).

In this regard, LADWP (2013) has developed the Owens Lake Master Project, which aims to

simultaneously fulfill dust control obligations, reduce lakewide water use by at least 50%, and

protect, create, and enhance habitat for waterbirds. The project proposes to use a mix of

tillage, vegetation, water, gravel, roads, and brine to control dust. The Owens Lake Habitat

Working Group discussed the concept of a "habitat suitability index" as a means to quantify the

habitat value of particular dust mitigation techniques for different guilds of waterbirds. This led

to the development of a Habitat Suitability Model (HSM) that first documents the baseline

value of habitat (dust and non-dust project areas) for species occurring at Owens Lake. The

HSM then can be used to predict the effect on the habitat value of dust control units when

modified by management activities and to monitor the value of the dust control project (and

non-dust control areas) to maintain the lake’s overall baseline habitat value (LADWP 2011). Five

waterbird guilds (groups of species with similar habitat requirements) were identified that are

important at Owens Lake: diving waterbirds, breeding waterfowl, migrating waterfowl,

breeding shorebirds, and migrating shorebirds (LADWP 2011). An HSM was then developed for

each guild based on information about preferences for important habitat variables on Owens

Lake from data and observations up to 2010. Where data or observations specific to Owens

Lake were not available, habitat preferences were obtained from peer-reviewed literature or

expert opinion.

The HSMs use rule-based classification algorithms to assign a habitat value for each guild in

each Dust Control Area management unit (DCA). Key parameters for HSMs across all guilds

were water depth, salinity (reflecting invertebrate abundance), and seasonal availability, but,

depending on guild, also included proportion of islands and dry area, extent of vegetation,

vegetation structure, and microtopography. Habitat suitability values were assigned according

P a g e | 7

to habitat parameters that reflect known resource requirements and habitat preferences for

each guild. These parameters were selected by LADWP in cooperation with outside experts

(LADWP 2011). Each parameter was measured at each DCA, either in the field or by remote

sensing across multiple seasons starting in 2010. In addition, bird use data have been collected

at each DCA on 7 survey events per year between 2012 and 2014.

Although the general HSM approach has been reviewed by an expert commission of scientists

and deemed effective, LADWP and the Habitat Working Group identified the necessity to

validate and refine the HSM predictions using new bird survey data and habitat measurements.

In August 2015 Point Blue Conservation Science (hereafter “Point Blue”) was contracted to use

these new data to rigorously evaluate the HSMs, to either validate their predictions or suggest

refinements where necessary. Point Blue and LADWP used bird and habitat data from 2012 to

2014 for HSM validation with three specific goals: (1) to evaluate the weights assigned to each

of the parameters in the HSM models for each avian guild; (2) suggest adjustments to

parameter weights; and (3) refine the habitat suitability and habitat area calculations. Point

Blue was also tasked with considering the potential for using other unmeasured environmental

variables that may strongly influence guild abundance.

The end goals of the work addressed in this report are: (1) to ensure that habitat suitability

values calculated from refined HSMs match observed bird abundances as closely as possible,

and (2) to recommend additional monitoring and management actions that will help to ensure

that habitats for avian guilds persist into the future under an adaptive management approach

allied with the Master Project. Within the rule-based framework of the existing HSM approach,

these recommendations will be both feasible and practical to implement as part of Owens Lake

monitoring and management plans. Managing and maintaining bird use at Owens Lake will

require maximizing habitat value while achieving water conservation objectives, and

improvements to the predictive ability of HSMs will help Owens Lake managers optimize

conditions for wildlife in a water-efficient manner.

P a g e | 8

Section 2: Owens Lake ecology, habitat and bird monitoring

Invertebrate food sources

Food availability is a key driver of population dynamics, and, hence, knowledge of aquatic

organisms that serve as the primary prey for waterbirds should be incorporated in the

development of management actions intended to provide habitat for waterbirds at Owens

Lake. Recent studies of aquatic invertebrates at Owens Lake have established their abundance,

diversity, ecological tolerances, and, hence, the habitat characteristics that best support food

resources for waterbirds (Herbst 1996, 2001, 2003, unpubl. data). Over 100 aquatic

invertebrate taxa have been found in surveys of natural and artificial aquatic habitats on and

around the Owens Lake playa. Brine flies (Ephedra hians and E. auripes) are the most common

food resource for birds foraging on saline waters on the Owens Lake playa; the former species

dominates at higher, the latter at lower, salinities. Sustained surface-water habitat on the order

of at least a few months duration is necessary for productive and diverse invertebrate

communities to develop (Herbst 2001). Experimental microcosms showed that salinities in the

range of 25–75 g/L promote the most productive habitats, for both benthic algae and brine

flies, but lower salinity environments serve both as reservoirs of insect diversity and source

flows. Hence, management of habitat for sustainable production of brine flies over an

intermediate salinity range would likely provide optimum conditions for waterbirds foraging in

restored aquatic habitats at Owens Lake. See Herbst (2001) for a ranking of habitats by a

combination of salinity and stability (ephemeral vs perennial).

Key waterbird species

Even when its overall habitat quality had reached a nadir prior to rejuvenation by shallow

flooding of the playa beginning in 2001, Owen’s Lake was considered an Important Bird Area in

California (Cooper 2004, http://netapp.audubon.org/IBA/Site/213). Although not yet so

designated, Owens Lake currently meets the criteria for a site of importance in the Western

Hemisphere Shorebird Reserve Network (www.whsrn.org/selection-criteria) as documented by

recent shorebird and waterbird surveys at the lake (LADWP 2014a,

(http://esaudubon.org/owens_lake/OL_Spring_Big_Day_Counts_Compared.pdf). Surveys in

1978 (Henderson and Page 1981) documented Owens Lake as a key breeding area for the

P a g e | 9

Snowy Plover (Charadrius nivosus), a California Bird Species of Special Concern (Shuford et al.

2008). After the initiation of shallow flooding to control fugitive dust in 2001, plover numbers

increased from lows of about 100–200 birds from 1988–2001 to about 360–740 (mean 545)

from 2004–2014 (Ruhlen et al. 2006, LADWP 2014b). Numbers within the latter range represent

the highest totals for the Snowy Plover at any inland or coastal site in California. The Snowy

Plover remains a focal species for conservation at Owens Lake, and lakewide surveys will

continue to be conducted periodically to fulfill mitigation monitoring requirements (GBUAPCD

2008).

Since shallow flooding began, lakewide surveys over the last decade have documented tens of

thousands of waterbirds at Owens Lake during periods of peak occurrence in spring, fall, and

winter (LADWP 2014a,

(http://esaudubon.org/owens_lake/OL_Spring_Big_Day_Counts_Compared.pdf). The most

numerous species at Owens Lake are the Northern Shoveler, Ruddy Duck, Eared Grebe,

American Avocet, small calidrid (mostly Least [Calidris minutilla] and Western [C. mauri])

sandpipers, and California Gull (Larus californicus). These also are the species that dominate at

many of the terminal lakes elsewhere in the Great Basin (Jehl 1994, 1996; Shuford et al. 2002).

The abundance of phalaropes at Owens Lake currently is modest, unlike their great abundance

at many saline lakes, but this likely reflects both a paucity of shallow water at Owens Lake

during their periods of peak occurrence in early fall (July–Sep) and fall surveys (beginning in late

Aug) coming after Wilson’s Phalaropes typically reach peak numbers in late July to early August.

Waterbirds at Owens Lake have been grouped into five guilds: diving waterbirds (DWB),

breeding waterfowl (BWF), migrating waterfowl (MWF), breeding shorebirds (BSB), and

migrating shorebirds (MSB). Each guild consists of 5–21 species with similar habitat

requirements and, hence, habitat use at Owens Lake (LADWP 2011). In four of the five guilds, at

least one of the most abundant species is among the salt-tolerant species listed above. For

these four guilds the abundant species are more than 10x as numerous as the total number of

individuals of the other species combined. The exception to this pattern is the breeding

waterfowl guild where the most abundant species, Gadwall, is only twice as numerous as the

P a g e | 10

total number of individuals of the four other species, though it too is the most salt tolerant

member of its guild (Jehl 2005).

Dust Control Area (DCA) management

The Owens Lake dust control season is between 16 October and 30 June. This is a period of

both high winds and formation of highly emissive crust on the Owens Lake playa. During most

of the dust season DCAs are required to be a minimum of 72% wet. This is accomplished by

maintaining ponds at target levels using lateral irrigation lines to account for evaporation and

promote relatively static habitat conditions. However, areas with a required control efficiency

of 99% (the vast majority of DCAs) receive reduced irrigation on 16 May to decrease wet cover

to a minimum of 60%, with complete shut-off of water application on June 30. DCAs that are

irrigated with laterals and have Snowy Plover broods or nests are slowly ramped down to allow

plovers to complete their nesting cycle.

At the end of the dust season ponded water will persist and decrease in depth and extent over

time based on evaporation rates and site-specific factors. Therefore variation in habitat

conditions among DCAs can be significant in the non-dust season. Some site specific factors that

affect water persistence include pond salinity, proximity to off-site water flowing in to the DCA

or water applied up-gradient slowly flowing subsurface then collected in lower ponds, and the

consolidation of existing water. Freshwater DCAs are typically the highest elevation DCAs, being

adjacent to the historic shoreline and typically in sandier soils. Given this location they are often

adjacent to areas with spring or artesian well flows on the edge of Owens Lake (T30-1, T28, and

T1A-2). Such DCAs benefit from additional water in the summer dry periods, which extends

their ability to maintain hospitable levels of salinity on the lake for longer periods. Other areas

may have prolonged water persistence from maintenance flows for Snowy Plover habitat or

from up-gradient sheet flow areas draining to ponds. These DCAs are essentially sub-irrigated

after actual irrigation ceases (T25S and T36-1). Some ponds are used to consolidate water from

adjacent areas to produce the lowest evaporative surface after the dust season. These locations

have the longest sustained water into the summer (T30-2). Two ponds have water applied for

P a g e | 11

operations year round to supply irrigation water to Managed Vegetation during the growing

season (T4-4 and T4-5).

The period during the late August bird survey is both the driest and the most variable time of

year. Some shallow flood ponds have water persisting from the previous dust control season (as

described above) but others dry completely. This start-up period of dust control is also the most

dynamic. Some ponds start filling as early as late August causing DCAs to go from salt flats to

operational ponds in short succession to achieve compliance by 16 October. The late

September bird survey occurs when DCAs are approaching wetness compliance levels, with

most ponds at or above target levels and lateral shallow-flood DCAs coming on-line. The late

October bird survey corresponds well to measured habitat conditions in October. The spring

bird surveys in March, April, and May correspond well to habitat conditions measured in May

unless there was an operational shut-down in specific DCAs to meet the needs of construction

or operational emergencies.

Owens Lake habitat and bird monitoring

From 2012 to 2014, standardized area-search surveys of birds have been conducted within the

entire Owens Lake Dust Mitigation Project Area seven times per year (LADWP 2014a). There

was one survey in winter (Jan), two during spring migration (Mar, Apr), one during the breeding

season (May), and three during fall migration (Aug, Sep, Oct). Each of these comprehensive

surveys is a composite of complete censuses of all individual waterbirds observed within each

of the DCAs. Collectively, over 700,000 individuals were recorded during these surveys, with

peak numbers coinciding with spring and fall migration periods. We used the counts from a

subset of these surveys as independent samples in our summary and modeling analyses

(described below), and thus our survey events consist of single visits to a DCA rather than a

metric summarizing counts across multiple visits (e.g. by averaging count values across months

or years).

Measured habitat variables included in the HSMs are primarily water parameters, including:

depth, salinity, and presence or absence assessed monthly. Other HSM variables, depending on

guild, include the proportion of the DCA area made up by dry land, island area, vegetation cover

P a g e | 12

and structure, and microtopography. Habitat suitability values are assigned according to habitat

parameters that reflect known resource requirements and habitat affinities for each guild

(LADWP 2011).

Habitat measurements were conducted using a variety of methods, including GIS and remote

sensing analyses, fixed water-level meters, and in situ field measurements with hand-held

instruments. Methods of habitat variable generation are described in detail in the Habitat

Analysis Report (LADWP 2015). For some habitat variables, a potential source of error comes

from the timing of measurements or estimates not coinciding with the bird survey dates. The

bulk of these inconsistencies is the result of variation in water depth across the year. Although

water additions occur largely in spring and early fall, evaporation occurs throughout the year,

leading to a dynamic pattern of water depth and, crucially, salinity. Both depth and salinity are

measured twice per year, during periods that are generally representative of spring and

summer (May) or fall (November). However, changes during water additions can lead to vastly

different water depth and salinity conditions over the course of the season particularly in the

non-dust season and upon start-up and may or may not be representative of conditions during

the avian survey events as described above. Other variables such as microtopography and

vegetation cover were assessed from a single date of imagery and used for all years of avian

surveys; while relatively constant, these parameters will vary over time.

In addition, some variables, such as salinity, are recorded as a DCA-wide average, which may

represent the general conditions of the cell adequately but will not inform the HSM about the

distribution or variability of those measurements within the DCA. Similarly, other

measurements are recorded as proportion or percentage of the DCA total area, or proportion

of the total water area, and also will not provide the HSM with information on the distribution

or variability of those values within the DCA. Thus one key implicit assumption of our model

fitting to inform HSM parameterization is that the variability of these values within the DCA is

of lesser influence on the guild abundances than the DCA-wide average, or, stated differently,

that the effects of these variables are strong enough on average and that we have enough data

points to overcome the effects of spatial variability within the DCAs. This is in part why we

P a g e | 13

chose to evaluate the abundance estimates on a per survey basis rather than for example

averaging across multiple visits, namely to generate a larger sample size albeit with more

variability

Description of guilds: Breeding Waterfowl (BWF)

Species abundance in this guild is dominated by Gadwalls, with a relatively large number of

Mallards, followed by fewer Green-winged and Cinnamon teal, and Northern Pintails (Table 1).

This guild has much lower abundance overall compared to other guilds. May survey data only

were used to inform validation and refinement of the HSM. The correlation between Gadwall

abundance and the abundance of the other four species combined is high at R = 0.67, indicating

that this guild is cohesive in terms of habitat use at Owens Lake.

Table 1: Total number of individuals of various species of Breeding Waterfowl counted within the study area by month, 2012–2014, and total number of DCAs in which at least one individual was recorded during the May breeding-season surveys.

Species January March April May August September October #DCAs (May)

Gadwall 1347 1521 1996 1308 37 363 2242 33

Mallard 117 120 153 522 185 191 207 30

Cinnamon Teal

19 1856 805 60 1 83 1 12

Northern Pintail

592 127 100 22 21 350 437 4

Green-winged Teal

1572 1322 278 91 25 232 2152 7

Total individuals

3647 4946 3332 2003 269 1219 5039 40

The relative abundance of species in the BWF guild at Owens Lake is generally consistent with

the relative abundance of these species in the Eastern Sierra overall. Gaines (1988) reported

the Gadwall appeared to be the most numerous breeding duck throughout the valleys at the

base of the Sierra. Although no breeding waterfowl are particularly adapted to saline lakes, the

Gadwall has the highest salinity tolerance of any dabbling duck and consequently it is the most

P a g e | 14

numerous breeding duck at saline Mono Lake (Jehl 2005, LADWP 2015) and Owens Lake (Table

1).

At most locations in the species’ range, Gadwall feed on submerged aquatic vegetation, but at

Mono Lake they feed extensively on brine flies (particularly the pupae) and concentrate at

freshwater inflows, freshwater ponds, and brackish ponds. The diet of the Gadwall at Owens

Lake is unknown but likely very similar to that at Mono Lake given that aquatic habitat on the

Owens playa has very limited aquatic vegetation and brine flies are the dominant invertebrate

in saline ponds (see above). Other breeding waterfowl at Mono Lake also concentrate at

freshwater inflows and freshwater ponds, but not at brackish ponds (LADWP 2003–2015).

Gaines (1988) reported that, after Gadwall, Mallards and Cinnamon Teal were the next most

numerous breeding ducks in the Eastern Sierra; other less numerous dabbling ducks breeding in

the region include the Green-winged Teal, Northern Pintail, and American Wigeon. Of the total

number of broods of the various dabbling ducks recorded on waterfowl monitoring surveys at

saline Mono Lake from 2002–2014, 78% were Gadwall, 14% Mallard, 5% Green-winged Teal, 3%

Northern Pintail, and 1% Cinnamon Teal (LADWP 2003–2015). Collectively this information

suggests that although the Gadwall is the most numerous breeding duck at Owens Lake, the

validation of the HSM for the BWF guild using total abundance for all species should also

represent habitat for other less numerous species of dabbling ducks.

Description of guilds: Migrating Waterfowl (MWF)

Species abundance in the MWF guild is heavily dominated by Northern Shoveler with relatively

few records of other species included in the BWF guild, and very few other non-breeding

species (Table 2). Correlation between Northern Shoveler abundance and the sum of individuals

from all other species is fair (R = 0.30), and thus validation of the HSM is likely less

representative of the entire guild compared to that for breeding waterfowl.

P a g e | 15

Table 2: Total number of individuals of various species of Migrating Waterfowl counted within the study area by month, 2012–2014, and total number of DCAs in which at least one individual was recorded during the March/April and September/October migration-season surveys.

Species January March April May August Sept-ember

Oct-ober

#DCAs (March/April)

#DCAs (Sept /Oct)

Snow Goose 185 294 0 0 0 0 201 1 7

Ross's Goose

0 0 0 0 0 0 1 0 1

Cackling Goose

0 0 0 0 0 0 4 0 1

Canada Goose

28 0 2 0 0 0 6 1 1

Tundra Swan

4 0 0 0 0 0 0 0 0

Wood Duck 0 0 0 0 0 3 0 0 1

Gadwall 1347 1521 1996 1308 37 363 2242 27 21

American Wigeon

99 155 136 13 1 75 133 14 15

Mallard 117 120 153 522 185 191 207 32 26

Blue-winged Teal

0 12 5 4 0 68 2 5 4

Cinnamon Teal

19 1856 805 60 1 83 1 31 10

Northern Shoveler

4370 7268 1631 212 8027 74397 104690 30 55

Northern Pintail

592 127 100 22 21 350 437 13 24

Green-winged Teal

1572 1322 278 91 25 232 2152 17 27

Unidentified Teal

0 0 3 10 6 0 0 1 0

Total individuals

8333 12675 5109 2242 8303 75762 110076 44 58

That the Northern Shoveler was the dominant species in the MWF guild at Owens Lake (96% of

total in fall; Table 2) reflects the pattern at other saline lakes. For example, at Mono Lake in fall

2014, shovelers accounted for 72% of all MWF, with Mallard the only other species that

accounted for more than 10% of the total (LADWP 2003-2015). By contrast, the 2014 fall totals

P a g e | 16

for MWF at the nearby freshwater reservoirs of Bridgeport Lake and Crowley Lake were more

evenly spread among 5 or 6 species. Northern Shovelers represented 36% and 30% of total

MWF in fall at these reservoirs, respectively, with an additional 63% spread among five other

species at Bridgeport and 66% among four other species at Crowley.

The high proportion of Northern Shovelers at Owens and Mono lakes appears to reflect the

species’ morphology, foraging habits, and predominately animal diet (Dubowy 1996). Shovelers

have a large spatulate bill with conspicuous comblike lamellae that function to sieve out

invertebrates in the water column. Although the shoveler diet has not specifically been studied

at Owens and Mono lakes, it likely mirrors the lakes’ dominant invertebrates: brine shrimp and

brine flies at Mono Lake, brine flies at Owens Lake. The diet of many other dabbling duck

species is predominantly plant matter and seeds, which are much more prevalent in freshwater

habitats than at saline lakes, but, as described for breeding Gadwall at Mono Lake, they may be

forced to depend more on invertebrates at Owens Lake given the paucity of aquatic vegetation

in flooded areas of the playa. A dependence on common food resources suggests that the HSM

refinements, though influenced largely by the abundance of the Northern Shoveler, may be

representative of the entire guild.

Description of guilds: Breeding Shorebirds (BSB)

Species abundance in the BSB guild is heavily dominated by the American Avocet, followed by

Snowy Plover, and relatively few records of other species (Table 3). We suggest removing four

species in this guild from future analyses based on issues related to timing of migration

(Spotted Sandpiper, Willet, Wilson’s Phalarope records in May are likely not breeding

individuals), Owens Lake occurring at the edge of the species ranges (Long-billed Curlew, Willet,

and Wilson’s Phalarope), and habitat associations (Curlew nests in grasslands). These four

species (in italics in Table 3) are very likely not breeding on the Owens Lake playa, and very few

individuals were recorded so the effect on HSM validation is negligible. We also note that the

two most common species, American Avocet and Snowy Plover, use different habitats. A strong

indication of the distinct habitat associations of these species is the relatively low correlation of

abundance of the American Avocet with the sum of individuals of all other species (R=0.21), and

the lack of correlation (R=0.03) between Snowy Plover abundance with abundance of the rest

P a g e | 17

of the guild. Thus validation and refinement of the HSM is compromised by the two most

abundant species using distinct habitats.

Table 3: Total number of individuals of various species of Breeding Shorebirds counted within the study area by month, 2012–2014, and total number of DCAs in which at least one individual was recorded during the May breeding-season surveys. Species in italics were removed from the HSM validation and refinement.

Species January March April May August September October #DCAs (May)

Black-necked Stilt

0 52 515 183 138 83 15 7

American Avocet

3601 67213 35306 5731 4767 12322 9148 60

Snowy Plover 20 77 209 1298 82 38 74 47

Killdeer 63 101 19 39 92 231 729 12

Spotted Sandpiper

0 0 22 156 20 27 0 41

Willet 28 18 24 9 3 10 96 7

Long-billed Curlew

23 51 10 29 39 23 97 8

Wilson's Phalarope

0 1 666 261 3041 224 1 20

Total individuals

3735 67513 36771 7706 8182 12958 10160 71

Snowy Plovers and American Avocets both breed commonly at saline lakes, but they overlap

only partially in nest placement and very little in foraging habitat. At Owens Lake, numbers of

both species have increased substantially since the flooding for dust control began in 2002. The

following accounts summarize the ecology and patterns of habitat use of these two species at

Owens Lake.

Snowy Plover

In response to shallow flooding for dust control beginning in 2002, the number of Snowy

Plovers nesting at Owens Lake has increased substantially and the birds have shifted their

distribution and habitat use patterns around the lake in response to these changing conditions

(Ruhlen et al. 2006). Prior to flooding for dust control, most nests on the west side of the lake

were near creeks or seeps and were about evenly located at the edge of or in openings of

P a g e | 18

patches of salt grass (Distichlis spicata) or were on barren alkali flats. Nests found in natural

areas elsewhere around the lake were scattered on open dry alkali, and about two-thirds were

near distinctive features such as dry washes, sparse patches of salt grass, rocks, woody debris,

unimproved roadsides, or vehicle tracks.

After 2001, nest placement appeared to be strongly affected by the presence of artificially

flooded areas, which consist of shallow ponds, mudflats, and dry alkali crust, surrounded and

subdivided by roads and berms (Ruhlen et al. 2006). Prior to flooding, distances of nests from

water in natural areas in the eastern and southern parts of the lake averaged 468 m in 1999

and 379 m in 2001. After flooding commenced in 2002, the distances in the same natural areas

averaged 425 m, but in an area of playa broadly flooded for dust control the distances from

water averaged only 8 m. Similarly, the number of plover broods increased in artificially flooded

areas, which by 2004 accounted for 72% of all broods on lakewide surveys. In addition, flooding

of the lakebed for dust control significantly extended the duration of the plover’s breeding

season at Owens Lake by about a month. Although water depth within the flooded areas

ranged up to about 1 m, measurements in 2002 found the depth of water nearest plover nests

averaged 0.037 m versus 0.054 m at randomly selected points.

At inland sites, breeding Snowy Plovers feed along the shores of saline and alkaline lakes and on

playas (mostly at seeps and along streams) (Page et al. 2009). Most feeding is in shallow (1–2

cm deep) water or on wet mud or sand, but on playas some foraging also occurs on dry flats. In

osmotically stressful environments, Snowy Plovers may rely on the water content of

insectivorous prey and water-conservation behaviors, such as standing in pools, to avoid

drinking saline solutions; birds do drink fresh water when available.

American Avocet

Prior to flooding for dust control, American Avocets nested in only small numbers at scattered

locations around Owens Lake (M. Prather in Ruhlen et al. 2003). By the second year of shallow

flooding in 2003, the peak count in mid-June at the primary nesting area in Zone 2 was 2812

adults, 514 nests, and 72 broods (Ruhlen et al. 2003). On lakewide surveys from mid-May to

early June, 96% of adult avocets were in flooded dust control areas, the remainder at natural

P a g e | 19

sites (a few of which were not surveyed). Numbers of adult avocets on annual late May

lakewide surveys from 2003–2014 averaged 2656 (min.-max. = 1104–6135) individuals (LADWP

2014).

American Avocets wade to feed in shallow water (≤15–20 cm) but also forage while swimming,

at water depths up to approximately 25 cm; birds may forage in areas above the water’s edge

that retain a surface film of water on the substrate (Ackerman et al. 2013). In addition to

methods used in water and sediments, avocets at saline wetlands visually locate and peck at

brine shrimp near the water’s surface and brine flies along the shoreline. Avocets maintain

water balance in hypersaline environments apparently both by direct physiological and indirect

behavioral mechanisms. They likely derive most of their water requirements directly from their

prey and minimize their ingestion of salt water by straining prey. Newly hatched avocet chicks

have relatively large salt glands for osmoregulation in saline habitats, but high salinities can still

influence the health, growth, and survival of chicks.

Description of guilds: Migrating Shorebirds (MSB)

MSB abundance is dominated by a few species, primarily the American Avocet and Calidris

species, most of which are Least and Western sandpipers (Table 4). This guild is diverse, and

many of the species use distinct habitats from dry upland for Killdeer and Long-billed Curlew to

relatively deep water for phalaropes. Though both dominant species/species groups are

primarily found wading in shallow water, avocets tend to use deeper water than Calidris

sandpipers. The moderate correlation between abundance of the American Avocet with the

sum of individuals of all other species (R=0.28) but low correlation (R = 0.10) of calidrids

indicate the potential for issues with validation and refinement of the HSM due to the distinct

habitat associations within the guild.

Since shallow flooding for dust control began, lakewide surveys over the last decade have

documented tens of thousands of migrating shorebirds at Owens Lake during periods of peak

occurrence in spring and fall (LADWP 2014a,

http://esaudubon.org/owens_lake/OL_Spring_Big_Day_Counts_Compared.pdf; Table 4). These

P a g e | 20

species are also very numerous at most saline lakes in the Intermountain West during migration

(Shuford et al. 2002).

Table 4: Total number of Migrating Shorebirds individuals counted within the study area from 2012-2014 by month, and total number of DCAs where at least one individual was recorded during March/April and September/October migration season surveys.

Species January March April May August Sept-ember

Oct-ober

#DCAs (March /April)

#DCAs (Sept /Oct)

Black-necked Stilt

0 52 515 183 138 83 15 22 14

American Avocet

3601 67213 35306 5731 4767 12322 9148 60 55

Snowy Plover 20 77 209 1298 82 38 74 35 12

Semipalmated Plover

0 0 331 11 29 21 0 32 6

Black-bellied Plover

0 75 17 1 0 4 7 17 5

Killdeer 63 101 19 39 92 231 729 20 44

Spotted Sandpiper

0 0 22 156 20 27 0 13 11

Greater Yellowlegs

378 398 478 1 95 280 473 36 31

Willet 28 18 24 9 3 10 96 8 8

Lesser Yellowlegs

0 5 12 0 13 8 1 6 5

Whimbrel 0 5 95 43 0 0 0 11 0

Long-billed Curlew

23 51 10 29 39 23 97 11 19

Marbled Godwit

0 1 24 14 3 5 1 7 3

Sanderling 0 0 3 18 0 1 0 2 1

Dunlin 738 256 481 12 0 0 1369 25 23

Baird's Sandpiper

0 0 0 0 93 24 0 0 7

Least Sandpiper

20995 11753 31128 26 5510 10655 17461 56 54

Western Sandpiper

2358 663 20153 105 2310 5885 2360 37 39

Unidentified Calidris

546 3628 63138 16 928 746 4940 43 17

Long-billed Dowitcher

12 523 404 5 16 16 123 28 12

P a g e | 21

Unidentified Dowitcher

0 6 400 0 4 0 0 9 0

Wilson's Phalarope

0 1 666 261 3041 224 1 25 10

Red-necked Phalarope

0 0 359 2362 2164 4619 36 21 33

Unidentified Phalarope

0 0 284 95 434 0 0 3 0

Total individuals

28762 84826 154083 10416 19790 35225 36931 73 63

This group of species varies considerably in their depth preferences for foraging, ranging from

dry or wet mud (e.g., plovers) to on the water’s surface at any depth (e.g., phalaropes); for

most but not all species, leg length is a good gauge of their depth preferences in water, as is bill

length in terms of depth they will probe into the substrate for invertebrates. They also range

considerably in their salinity tolerances from those associated mostly with fresh water (e.g.,

Killdeer) to those highly adapted to hypersaline conditions (American Avocet, Snowy Plover),

though the occurrence of virtually all of these species on coastal estuaries during migration or

winter indicates they are tolerant of at least moderate salinities. The wide range of foraging

niches among the many species in this guild may pose some problems for model validation, but

continuing to provide flooded areas with a range of depths and moderate salinities would be

likely to maintain current diversity of migrant shorebirds at Owens Lake.

Description of guilds: Diving Waterbirds (DWB)

Species abundance of the DWB guild is dominated by the Ruddy Duck and Eared Grebe, with

the remaining species being much less numerous. Some species included in this guild (Clark’s

Grebe, Common Merganser, Hooded Merganser, Pied-billed Grebe, Red-breasted Merganser,

and Western Grebe) forage primarily on fish, of which there are none at Owens Lake. We

removed these species from further analyses given that there is no, or very limited, suitable

foraging habitat and thus we judged their records (of which there are very few) would not

inform the HSM or management decisions at Owens Lake. Abundance of both the Eared Grebe

(R = 0.25) and Ruddy Duck (R = 0.27) is moderately correlated with guild abundance, and thus

the guild is a relatively cohesive group that is appropriately represented by the HSM.

P a g e | 22

Table 5: Total number of individuals of various species of Diving Waterbirds counted within the study area by month, 2012–2014, and total number of DCAs in which at least one individual was recorded during the March/April and September/October migration-season surveys. Species in italics were removed from the HSM validation and refinement.

Species January March April May August Sept-ember

Oct-ober

#DCAs (March /April)

#DCAs (Sept /Oct)

Canvasback 4 2 0 0 0 0 7 1 1

Redhead 178 575 384 227 0 16 85 13 5

Ring-necked Duck

0 11 16 2 0 7 6 6 6

Lesser Scaup 2 25 4 2 0 0 24 8 3

Bufflehead 319 915 148 7 0 0 261 25 19

Common Goldeneye

4 1 0 0 0 0 0 1 0

Hooded Merganser

0 0 0 0 0 0 10 0 4

Common Merganser

0 0 4 2 0 0 2 2 2

Red-breasted Merganser

0 0 1 2 0 0 2 1 1

Ruddy Duck 10083 19473 6053 577 6 1275 16912 40 45

Pied-billed Grebe

0 1 0 1 0 1 3 1 3

Eared Grebe 176 2809 19705 2948 71 14718 37986 51 60

Western Grebe

0 0 10 2 0 10 35 6 22

Total individuals

10767 23812 26329 3770 77 16027 55333 57 60

Of the two numerically dominant species in this guild at Owens Lake, the Eared Grebe is highly

adapted to saline lakes during migration (Jehl 1988) and the Ruddy Duck uses both saline and

freshwater habitats (Brua 2002, LADWP 2003-2015). In addition to Mono and Owens Lake,

saline water bodies used by Ruddy Ducks in California include hypersaline agricultural

evaporation ponds in the southern San Joaquin Valley and the moderately saline Salton Sea

(Brua 2002). In the Eastern Sierra, fewer Ruddy Ducks use Bridgeport Lake than either Mono

Lake or Crowley Lake (LADWP 2003-2015). From 2002–2014, the mean annual number of

P a g e | 23

Ruddy Ducks in fall was 11,003 at Mono Lake and 7,474 at Crowley Lake, but this difference was

not statistically significant given high annual variation in numbers.

The diets of both the Eared Grebe and Ruddy Duck are dominated by invertebrates. The overall

diet of the Eared Grebe includes a wide variety of aquatic prey, principally invertebrates,

including small crustaceans (especially brine shrimp) and insects (Cullen et al. 1999). At Mono

Lake and other fall staging areas, these grebes feed largely on the open lake on free-swimming

brine shrimp (down to depths of up to 9 m) but also dive to pluck larval and pupal brine flies

from firm substrates, peck or skim prey from the surface of the water, (or lunge to take

airborne flies), or glean adult flies from exposed tufa (Jehl 1988, Cullen et al. 1999). Ruddy

Ducks eat primarily aquatic insects, crustaceans, zooplankton, and other invertebrates, which

they obtain mainly by diving in relatively shallow water; limited information suggests they

strain benthic substrates to obtain prey (Brua 2002). The diet of Ruddy Ducks has not been

documented at saline lakes, but at Owens and Mono Lakes it likely consists of the dominant

invertebrate at those sites—brine flies at Owens and brine flies and brine shrimp at Mono. The

cohesiveness of the DWB guild may reflect the similar diet of the two principal species and the

few individuals present of other diving species.

Section 3: HSM Validation and Refinement Methods

Each HSM is constructed by combining individual Suitability Index Values (SIVs) for several

habitat parameters into a single Habitat Suitability Value (HSV) for each individual DCA. These

HSVs are then multiplied by the acreage of the cell to generate Habitat Value Acres. In the case

of DWB, acreage of the ponds is used instead of DCA area. Value Acres are the metric by which

habitat amounts at Owens Lake are compared over time to assess the effects of management

and projects like the Master Plan. SIVs are assigned to different levels of each parameter in the

model based on guild habitat preferences. The SIVs range between possible values of 0 to 1

that indicate the suitability of each component parameter. For example, in the shorebird

habitat models, shorebird preference for shallow water is reflected by assigning SIV=1, whereas

other water depths are assigned values less than 1. Each parameter’s SIVs are combined

P a g e | 24

mathematically to obtain the HSV, which also ranges on a scale of 0–1, where 0 indicates

completely unsuitable habitat and 1 indicates fully suitable habitat.

The process of refining the HSMs for the Owens bird guilds took several steps. First, using the

bird survey and habitat data generated by LADWP, we conducted a validation of the HSM

components by comparing the ability of the HSV and each individual SIV at distinguishing survey

events that recorded different guild abundances. We compared HSV and SIV values for groups

of survey events that recorded zero individuals, 10 or fewer, 11 to 100, and more than 100

using bean plots (Kampstra 2008), which display data distributions in a similar manner to box

plots. In addition to showing the mean and range of values as a box plot would, a bean plot also

displays the actual distribution of data points in an easily interpretable format. From these plots

we evaluated whether HSV or SIV increased with increasing abundances as expected, and

whether the range of suitability values was adequate or the SIV parameters needed to be

altered to correct any potential issues.

These HSV and SIV evaluations were followed by multivariate statistical model analyses fitting

guild density (abundance divided by habitat area for each DCA) to measured habitat

parameters. We employed two separate modeling approaches. We used Recursive partitioning

trees (known as RPART or “classification and regression trees”, Therneau et al. 2015) to provide

flexible non-parametric models that can identify relative variable importance, thresholds, and

ordered interactions among predictor variables that explain why groups of survey locations

tend to have higher abundance than others. We then used linear and linear mixed-effect

regressions (Bates et al. 2015) to assess the explanatory ability of the habitat measurements,

and to evaluate relationships between guild density and individual habitat variables through

partial dependency plots. We then used results from the partial dependency plots to inform SIV

parameterizations. Results of the regressions, including individual habitat variable coefficient

values and significance in relation to the other variables were also used to inform changes to

the combinations of individual SIV parameters in the HSMs. DCA was included as a separate

random effect in a mixed-effects linear regression model to compare habitat fixed effect

coefficients and explanatory power (R2 values) between models to assess non-independence of

P a g e | 25

repeatedly sampling DCAs and to see what the relative contribution of location and, by

extension, other unmeasured habitat and landscape variables is to the overall model fit. In all

cases, the addition of DCA random effects increased the R2 of the mixed effects model over the

linear model, but all fixed effects parameters were very similar and thus we plotted partial

dependency plots from the linear models only. All analyses were conducted in the statistical

package R version 3.2 (R Core Team 2014), using several packages for data processing and

visualizations (Venebles and Ripley 2002, Fox 2003, Wickham 2007, Kampstra 2008, Heiberger

2016). All decisions on changes to HSMs were made in collaboration with staff from Point Blue,

LADWP, and members the Owens Lake Habitat Working Group through numerous meetings,

discussions, and correspondence. HSM parameters were selected during these collaborations

using both the results of survey data fit to habitat measurements as well as expert opinion

augmented by published literature.

RPART models were fit to the dependent variable of guild density. The area used to calculate

density varied by guild based on whether both water and dry area or just water is used by the

guild. Density was calculated for both of the shorebird and waterfowl guilds by dividing total

abundance by total area of the DCA; for diving waterbirds we used total area of water in ponds

larger than 40 acres (16.2 ha). We limited diving waterbirds to ponds greater than 40 acres

based on observations that ponds smaller than 40 acres rarely harbored diving waterbirds

regardless of their other attributes (LADWP 2011.).

The independent variables included in RPART models included DCA acres; water acres; water

depth (as proportion of total DCA area, or pond area for the diving waterbirds guild) in four

depth classes (0–10 cm, 10–25 cm, 25–40 cm, >40 cm); presence or absence of sheet flow

water management; salinity (ranged from 0 mS/cm to maximum capped at 110 mS/cm, which is

the maximum value that certain measurement tools were able to record); monthly water

availability (presence or absence in each month); island area (as % of the DCA area covered by

delineated islands); dry area (proportion of the DCA area that is not covered by water);

microtopographic relief (proportion of the dry area of a DCA that is covered in each of three

relief categories: 0–3 cm, 3–10 cm, >10 cm); vegetation structure; vegetation cover extent; as

P a g e | 26

well as season (spring or fall, for migratory guilds only) and year of the survey event. Methods

used to generate the habitat measurements are described in the Owens Lake Habitat Suitability

Model report (LADWP 2011). The set of model input variables was modified slightly for the

diving waterbird guild. We limited the number of splitting levels in RPART trees to five and

counted the number of times each variable and up to three competing variables were selected

as a splitting factor in the final tree as a measure of variable importance (numbers represent

rounded percentages of the total number of splits).

Regression models fit the log-transformed density to the independent variables that were

either included in the RPART model variable importance list at values >2, or were included in

the original HSM (LADWP 2011). Density values were added to 1 and log transformed to reduce

the range of outliers while keeping the number of zero values the same. Reflecting the flocking

behavior of species in these guilds, there were several surveys for each guild with very high

abundances, and (depending on the size of the DCA) densities were recorded. There was no

clear pattern of which DCAs these very high counts occurred in, so we capped these values to

reduce their influence on the regression models. In addition, one data point was removed

where the water area was zero despite high recorded waterfowl and diving waterbird guild

abundance. To assess whether interactions between variables were creating issues with model

fit we calculated the variance inflation factor (Heiberger 2016), and removed variables with

values >6.0. We also plotted histograms of model residuals to evaluate fit. Plots were normally

distributed around 0.0 except for some positive skew indicating that some very large densities

were not predicted as accurately as more moderate density values. We feel that this is

acceptable with flocking behavior of these birds resulting in temporary inflations of abundance

that could be fleeting and not necessarily proportional to the habitat suitability or quality. From

the global regression model we performed a final model selection using stepwise removal of

individual variables with the lowest significance until AIC did not improve. We then compared

the coefficient values from this linear regression model to a linear mixed-effects regression

model including one additional variable, a random effect for DCA, and evaluated the difference

in the fixed effects coefficients to verify that each effect was in fact well supported. We also

compared the R2 of the linear and mixed effects models to evaluate what additional

P a g e | 27

explanatory power could be attributed to the effect of location (DCA), which would indicate the

potential for unmeasured site factors that may be influential.

Finally, once all SIV parameters were parameterized according to expert opinion alongside our

analyses described above, we evaluated the individual SIVs as well as composite HSV from each

HSM using regressions of all SIVs versus log guild abundance values. The purpose of evaluating

individual SIVs was to evaluate the relative contribution of each parameter to the total HSV

value and inform potential changes to the HSV calculation. The purpose of evaluating the HSV

was to assess the relative contribution of the calculated suitability value versus the DCA area

effect. To compare these results we report the R2 of log abundance versus habitat acres

(HSV*DCA acres) as well as log abundance versus HSV and DCA acres as separate independent

variables.

Section 4: Results

Breeding Waterfowl Guild

Basic summary metrics and exploratory data analysis

Of the 232 survey events (three visits to all 78 project area DCAs in May from 2012–2014) at

least one BWF individual was counted on 68 surveys, with zero detections on the remaining 164

surveys. Of the 68 surveys with detections, 25 had fewer than 10 individuals, 33 had 10–99

individuals, only 9 surveys recorded between 100–522 individuals, and the maximum count was

612 individuals. In examining the abundance data by survey, several small DCAs tended to be

the ones with the highest counts. There were only 14 survey events with guild abundance

counts larger than 60 individuals, and all but one of these counts occurred on a DCA smaller

than 300 acres. The one large DCA with high abundance counts, T18S, is 1166 acres in size, but

when counts were converted to density they were near average. BWF density ranged from 0.0–

2.68 individuals/acre (total DCA area). Mean density was 0.09 and median was 0.0

individuals/acre.

P a g e | 28

Bean plots show that the HSV as calculated in the original HSM formula did not reflect high

abundance very well, as average HSV was only 0.2 at locations with over 100 BWF individuals

(Figure 1). At locations with less than 100 individuals HSV was similar to locations where they

were absent. The range of HSVs for this guild using the original HSM was very small, reaching a

maximum of only 0.31. The original HSM predicted mostly zero HSVs, even at occupied sites.

Figure 1: Bean plot of Habitat Suitability Values (HSVs) across categories of abundance for BWF (absent = no BWF detected, other number ranges indicate the total count of individuals of BWF for that survey event The thickness of the blobs increase where multiple tick marks overlap at a similar SIV/abundance combination. The thick horizontal line in each vertical bar shows the average HSV for that group of data points, the thin dotted line shows the overall sample mean.

Individual HSM parameter bean plots show that few of the parameter Suitability Index Values

(SIVs) included in this model do increase with higher abundance (Figure 2). The variable that is

perhaps best at distinguishing high abundance locations is salinity. Average salinity SIV at

absent sites was 0.2, but at sites with more than 100 individuals it was 0.8. However, there

were numerous surveys where no BWF were detected but salinity SIV was 0.4 or 0.8. The

vegetation structure parameter SIV at locations where BWF were detected was higher on

average (about 0.4 regardless of how many individuals were detected) than locations where

they were absent (average = 0.2). Water depth did not distinguish absent from present

P a g e | 29

locations at all, and vegetated extent only slightly, though the range of this SIV was very small.

The other parameters (not shown) also did not to distinguish between absent and abundant

locations.

Figure 2: Bean plot of Suitability Index Values (SIVs) across categories of abundance for BWF (absent = no BWF detected, other number ranges indicate the total count of individuals of BWF for that survey event). The thickness of the blobs increase where multiple tick marks overlap at a similar SIV/abundance combinations. The thick horizontal line across each vertical bar shows the average SIV for that group of data points, the thin dotted line shows the overall sample mean.

P a g e | 30

BWF HSM refinement

Results of the RPART model fit showed support for salinity, vegetation, microtopography, and

water depth as having substantial influence on BWF abundance (Figure 3). Three pathways in

the RPART tree led to high density: 1) low salinity; 2) salinity higher than fresh water, low 3–10

cm microtopography area, and certain saltgrass vegetation types; 3) salinity higher than fresh

water, some 3–10 cm microtopography area, > 5% vegetation cover, and some water in the 25–

40 cm depth category. These high density surveys occurred on a small portion of the available

DCAs.

Figure 3: The BWF RPART tree shows that salinity, vegetation cover, microtopography, and vegetation structure types, and water depth can be used to differentiate high density from low density surveys. Variable importance ranking of the primary features that differentiate high from low density surveys: salinity (60%), microtopography (18%), vegetation cover and structure (10%), water depth (9%), and dry area (4%). Variable abbreviations: “VegCover” = vegetation cover, units are % of DCA area; “MT310cm” = microtopography 3-10 cm, units are

P a g e | 31

proportion on a scale 0.0-1.0; “VegStruc” = vegetation structure types, as described in LADWP 2011; salinity units are mS/cm.

Regression models showed that there were strong associations with the deepest water depth

category, salinity, island area, microtopography, and vegetation structure (Table 6). The salinity

effect was negative with a positive quadratic term indicating a strong preference for fresh

water that declines quickly to near zero density at the upper end of the salinity range. The

negative effect of island area does not fit with our biological understanding of this guild. The

removal of insignificant variables from the global model did improve AIC by 13 points yet also

led to a reduction in R2. Including DCA as a random effect in a mixed model drastically improved

both R2 and AIC, indicating that unmeasured local and/or landscape conditions have a large

effect on BWF density.

Based on the support for water depth, salinity, microtopography, and vegetation structure and

cover in the RPART and regression model results, we identified the following changes for our

refined HSM: increased suitability of deep water; increased suitability of low salinity and

hypersaline; adjusted suitability of vegetation cover to reflect higher suitability of intermediate

cover; gave full suitability to vegetation structure classes other than saltgrass types or non-

vegetated (Table 7). The negative association with 3–10 cm microtopography is biologically

unclear and based on expert opinion we chose not to include this variable in the HSM. The

relationship with island area is also unclear from regression model results, thus we based

parameterization of this variable based on experts’ biological understanding to reflect the

benefits of having at least some small amount of island area (if vegetated) for loafing or

nesting.

P a g e | 32

Table 6: Fit statistics (R2 and AIC) and model parameter coefficients for the BWF global regression model, including variables that were included in original HSM or supported by RPART model, and selected variable model after removing least significant variables until AIC no longer improves. See Appendix (Figures A1 and A2) for plots of the selected variable model salinity and vegetation cover effects.

Breeding Waterfowl

Global Model (R2=0.35, AIC=-217) Selected Variables (R2=0.29, AIC=-230)

Estimate Std.Error Pr(>|t|) Estimate Std.Error Pr(>|t|)

Intercept -0.03 0.07 0.68 0.04 0.04 0.28

DCA acres -0.23 0.21 0.26

DCA acres2 0.18 0.16 0.27

Water depth 10-25 cm

-0.01 0.10 0.96

Water depth 25-40 cm

0.12 0.14 0.38 0.15 0.12 0.21

Water depth >40 cm

0.14 0.08 0.09 . 0.14 0.07 0.05 *

Salinity -0.54 0.20 0.01 ** -0.52 0.17 0.00 **

Salinity2 0.44 0.16 0.01 ** 0.49 0.15 0.00 **

Water Persistence

0.01 0.01 0.34 0.01 0.01 0.30

Islands -0.25 0.17 0.15 -0.01 0.00 0.06 .

Islands2 0.09 0.15 0.57

Dry area -0.19 0.25 0.45

Dry area2 0.04 0.18 0.82

Micro-Topography 0-3 cm

0.16 0.20 0.42

Micro- Topography 3-10 cm

-0.21 0.12 0.07 . -0.12 0.06 0.05 *

Micro- Topography >10 cm

0.37 0.26 0.15

Veg cover 0.12 0.20 0.56 0.27 0.17 0.11

Veg cover2 -0.47 0.20 0.02 * -0.60 0.15 0.00 ***

Veg (HHSD) 0.09 0.09 0.33

Veg (LGSD) 0.04 0.04 0.40

Veg (MHLSS) 0.05 0.10 0.66

Veg (NV) 0.01 0.05 0.86

Veg (SD) 0.02 0.06 0.80

Veg (SDAS) 0.11 0.05 0.02 *

Year 2013 0.01 0.02 0.73

P a g e | 33

Year 2014 0.01 0.02 0.65

Mixed model including DCA random effect: (R2=0.84, AIC=-286)

The new refined HSM parameters did improve fit of log guild abundance to the habitat

assessment parameters (Table 7). The revised HSV calculation is similar to the original HSM,

only that water availability SIV, islands SIV, vegetation structure SIV, and vegetated extent SIV

have been combined using an arithmetic mean calculation: HSV = (Water depth SIV * Salinity

SIV)0.5 * (Vegetation cover SIV + Vegetation structure SIV + Water Availability SIV + Islands SIV

)/4.

Table 7: Comparison of original Habitat Suitability model parameters for Breeding Waterfowl to those of the refined model.

Parameters Habitat Value

Measurements

Original BWF Habitat Suitability Model

Suitability Index Value (SIV)

New BWF Habitat Suitability Model

Suitability Index Value (SIV)

Water depth

0cm (no water) 0-13 cm

13-30 cm >30 cm

0 * proportion of cell 0.8 * proportion of cell 1.0 * proportion of cell

0.01 * proportion of cell

[sum total*1.25]

0 cm 0.1 0-10 cm 0.5 10-25 cm 1.0 25-40 cm 1.0 > 40 cm 1.0

[sum total]

Salinity (average for

polygon)

0-5 mS/cm 5-15 mS/cm

15-50 mS/cm 50-70 mS/cm

70-100 mS/cm >100 mS/cm

0.4 0.8 1.0 0.6 0.4 0.0

1.0 1.0

(15-30mS/cm) 0.6 (30-50mS/cm) 0.4 (50-70mS/cm) 0.2

(70-100mS/cm) 0.1 (>100mS/cm) 0.0

Seasonal Water

Availability

Spring (February, March, April)

Summer (May, June, July)

Fall (August, September,

October)

Winter (November, December, January)

0.35

0.4

0.25

0.0 [sum total]

January 0.0 February 0.0 March 0.1 April 0.2 May 0.2 June 0.2 July 0.2 August 0.1 September 0.0 October 0.0

P a g e | 34

November 0.0 December 0.0

Island area (% of total DCA area)

<4% For each additional 1%

add 0.1 to a maximum of 1 at 10% and above

0.3

1.0

No islands 0.6 1% of pond area 0.7 2% of pond area 0.8 3% of pond area 0.9 4% of pond area 1.0

Vegetated extent (% of

cell)

> 60% 40-60% 25-40% 5-25% 0-5%

0.4 1.0 0.8 0.2 0.0

0.1 0.4 0.8 1.0 0.4

Vegetation Structure

HHSD AHLAS SDAS LGSD

MHLSS SD NV

1.0 0.8 0.5 0.2 0.2 0.1 0.0

1.0 1.0 1.0 0.1 1.0 0.1 0.0

regression of log abundance to habitat

value:

Habitat Acres R2 = 0.08 Habitat Suitability + Acres R2 = 0.20

Habitat Acres R2 = 0.21 Habitat Suitability + Acres R2 = 0.43

Changes to the HSM have increased the range of HSV values from 0–0.31 to 0–0.67 with many

fewer zero values. The reduction in zero values for HSV was a result of using the arithmetic

mean for a portion of the HSM rather than the geometric mean. Changes to the vegetation

cover, water depth, and island area SIVs have made it much less likely to calculate a low or zero

HSV score. The habitat acres regression improved from R2 =0.08 to 0.21, and habitat suitability

+ acres improved from R2 =0.20 to 0.43.

Migratory Waterfowl Guild

Basic summary metrics and exploratory data analysis

Of the 926 survey events at least one MWF individual was counted on 278 surveys, with zero

detections on the remaining 648 surveys. Of the 278 surveys with detections, 63 had fewer

than 10 individuals, 79 had 10–99 individuals, 92 had 100–999 individuals, and 44 had 1000 up

to a maximum count of 7062 individuals. In examining the abundance data by survey, a wide

range of DCA sizes were among the ones with the highest counts. There were 37 survey events

P a g e | 35

with guild abundance counts larger than 1500 individuals, and these were primarily on DCAs

ranging in size from 172 to 1166 acres. MWF density ranged from 0.0–27.5 individuals/acre

(total DCA area). Mean density was 0.61 and median density was 0.0 individuals/acre.

Bean plots show that the HSV as calculated in the original HSM formula increased only slightly

with abundance as average HSV was approximately 0.2 at locations where MWF was absent

compared to 0.35 at locations with over 100 individuals (Figure 4). Similarly, at locations with

fewer than 100 individuals HSV was only slightly higher than locations where they were absent.

Notably the majority of locations where MWF were absent have 0.0 HSV, whereas 0.0 HSVs

were a small minority of locations where they were present. Otherwise there was little

discrimination between abundance values based on HSV.

Figure 4: Bean plot of Habitat Suitability Values (HSVs) across categories of abundance for MWF (absent = no MWF detected, other number ranges indicate the total count of individuals of MWF for that survey event The thickness of the blobs increase where multiple tick marks overlap at a similar SIV/abundance combination. The thick horizontal line in each vertical bar shows the average HSV for that group of data points, the thin dotted line shows the overall sample mean.

Bean plots for individual HSM parameters show that only one of the parameter Suitability Index

Values (SIVs) included in this model, salinity, increased with higher abundance (Figure 5).

Average salinity SIV at absent sites was 0.45, whereas at the abundant sites with >100

P a g e | 36

individuals it was nearly 0.9. There were also numerous surveys where no MWF were detected

but salinity SIV was 1.0. The other parameter SIVs did not distinguish between absent and

abundant locations.

Figure 5: Bean plot of Suitability Index Values (SIVs) across categories of abundance for MWF (absent = no MWF detected, other number ranges indicate the total count of individuals of MWF for that survey event). The thickness of the blobs increase where multiple tick marks overlap at a similar SIV/abundance combinations. The thick horizontal line across each vertical bar shows the average SIV for that group of data points, the thin dotted line shows the overall sample mean.

P a g e | 37

MWF HSM refinement

Results of the RPART model fit show support for microtopography, salinity, season, water

availability, water area, water depth, and vegetation having substantial influence on MWF

density (Figure 6). There were two main branches that led to high density in the RPART tree.

The first split grouped a large number of surveys into a low density node based on the presence

of any area in 3–10 cm microtopography category. Within that branch the highest density set of

surveys occurred where September water was present, on fall surveys, in areas where the area

of water was over 376 acres, and where the proportion of shallow water was over 0.16. The

highest density group occurred on the other main branch, where salinity was less than 40

mS/cm, on fall surveys, with over 199 acres of water. A third relatively high density set of

surveys occurred on spring surveys, where vegetation cover was >21%.

Regression model results show that there were strong associations with DCA area, salinity,

season (fall higher), water depth, and microtopography (Table 8). The salinity effect was strong

and negative with a positive effect of the quadratic term indicating a threshold relationship.

Thus a strong preference for fresh water that declines quickly and plateaus at the higher end of

the salinity range. Density increases with increasing DCA area up to about 400 acres and then

the effect plateaus. The effects of water depth variables were relatively weak, showing positive

association with both the shallow and deep water categories. The effect of islands is positive

but weak.

Removing the non-significant variables from the global regression model had only a small effect

on model fit and AIC. The addition of DCA as a random effect in a mixed model also did not

improve AIC or R2 strongly, in contrast to BWF. This result implies that unmeasured location

and landscape effects are not strong factors influencing MWF abundance.

P a g e | 38

Figure 6: The MWF RPART tree shows that salinity, microtopography, season, water availability, water area, and vegetation cover can be used to differentiate high density from low density surveys. Variable importance ranking of the primary features that differentiate high from low density surveys: microtopography (30%), water area (13%), salinity (13%), season (16%), dry area (6%), vegetation cover (3%), water availability (3%), and water depth (5%). Variable abbreviations: “MT310cm” = microtopography 3-10 cm, “MT3cm” = microtopography 0-3 cm, “MTG10cm” = microtopography > 10 cm, units are proportion of DCA dry area on a scale 0.0-1.0; “SeptWat” = presence of water in September, units are binary, 1 for present and 0 for absent; “WaterAcr” = wet or water covered area, units are acres; “VegCover” = vegetation cover, units are % of DCA area; “Islands” = island area, units are proportion of DCA dry area on a scale of 0.0-1.0; Seasons are “Fll” = fall and “Spr” = spring; salinity units are mS/cm.

P a g e | 39

Table 8: Fit statistics (R2 and AIC) and model parameter coefficients for MWF global regression model, including variables that were included in original HSM or the RPART model, and selected variable model after removing least significant variables until AIC no longer improves. See Appendix (Figures A3 and A4) for selected-variable model plots of salinity and water acres effects.

Migrating Waterfowl

Global Model (R2=0.23, AIC=1173) Selected Variables (R2=0.23, AIC=1169)

Estimate Std.Error Pr(>|t|) Estimate Std.Error Pr(>|t|)

Intercept 0.38 0.07 0.00 *** 0.36 0.06 0.00 ***

DCA acres 1.41 0.52 0.01 ** 1.38 0.50 0.01 **

DCA acres2 -1.25 0.48 0.01 ** -1.36 0.47 0.00 **

Water depth 10-25 cm

0.20 0.16 0.21 0.25 0.15 0.10 .

Water depth 25-40 cm

0.11 0.18 0.55

Water depth >40 cm

0.25 0.12 0.03 * 0.29 0.11 0.01 *

Salinity -3.40 0.48 0.00 *** -3.39 0.48 0.00 ***

Salinity2 1.79 0.48 0.00 *** 1.78 0.48 0.00 ***

Season (spring)

-0.24 0.03 0.00 *** -0.24 0.03 0.00 ***

Islands 0.65 0.49 0.19 0.67 0.49 0.17

Islands2 0.83 0.47 0.08 . 0.83 0.47 0.08 .

Micro-Topography 0-3 cm

-0.27 0.27 0.31

Micro- Topography 3-10 cm

-0.19 0.15 0.21 -0.33 0.10 0.00 **

Micro- Topography >10 cm

-0.40 0.38 0.28

Year 2013 0.04 0.04 0.28 0.04 0.04 0.26

Year 2014 -0.05 0.04 0.20 -0.05 0.04 0.21

Mixed model including DCA random effect: (R2=0.37, AIC=1145)

Based on the support for water depth variables, salinity, water availability, water area, and

island area in the RPART and regression model results, we identified the following changes for

our preliminary refined HSM: increased suitability of deep water; reduced suitability of higher

salinity levels; and adjusted suitability of water availability to increase summer and fall water

P a g e | 40

values. We added an additional water area SIV parameter to reflect lower suitability of water

areas under 200 acres (Table 9).

The HSV calculation is modified so that salinity is no longer averaged with island area and

seasonal water, which are included with water area using an arithmetic mean: HSV = Water

depth SIV * Salinity SIV * (Water Availability SIV + Island Area SIV + Water Area SIV)/3. The new

refined HSM parameters (Table 9) do improve fit of log guild abundance to the habitat

assessment parameters. Habitat acres improved from R2=0.12 to 0.26, and habitat suitability +

acres from 0.18 to 0.35. These changes to the HSM have resulted in little change to the range of

HSV values (maximum of 0.86 using original HSM, 0.80 with refined HSM), although some HSV

values for individual DCAs have changed considerably.

Table 9: Comparison of original Habitat Suitability model parameters for Migrating Waterfowl to those of the refined model.

Parameters Habitat Value

Measurements

Original MWF Habitat Suitability Model

Suitability Index Value (SIV)

New MWF Habitat Suitability Model

Suitability Index Value (SIV)

Water depth

0cm (no water) 0-10 cm

10-25 cm 25-40 cm >40 cm

0 * proportion of cell 0.8 * proportion of cell 1.0 * proportion of cell

0.01 * proportion of cell 0.0

[sum total*1.25]

0.1 0.5 1.0 1.0 0.8

[sum total]

Salinity (average for

polygon)

0-5 mS/cm 5-15 mS/cm

15-50 mS/cm 50-70 mS/cm

70-100 mS/cm >100 mS/cm

0.8 1.0

1.0 0.5 0.1 0.0

1.0 1.0

(15-30mS/cm) 0.8 (30-50mS/cm) 0.6 (50-70mS/cm) 0.3

0.1 0.0

Seasonal Water

Availability

Spring (February, March, April)

Summer (May, June, July)

Fall (August, September,

October)

0.35

0.4

0.25

January 0.025 February 0.05 March 0.05 April 0.05 May 0.025 June 0.025 July 0.1

P a g e | 41

Winter (November, December, January)

0.0

[sum total]

August 0.15 September 0.2 October 0.2 November 0.1 December 0.025

Water area

Increasing suitability up to ~200 acres

NA

0 acres 0.0 20 acres 0.1 40 acres 0.2

…. 180 acres 0.9 200 acres 1.0

Island area (% of total DCA area)

<4% For each additional 1% of

cell area add 0.1 to a maximum of 1 at 10% and

above

0.3

1.0

No islands 0.6 1% of pond area 0.7 2% of pond area 0.8 3% of pond area 0.9 4% of pond area 1.0

regression of log abundance to habitat

value:

Habitat Acres R2 = 0.12 Habitat Suitability + Acres R2 = 0.18

Habitat Acres R2 = 0.26 Habitat Suitability + Acres R2 = 0.35

Breeding Shorebird Guild

Basic summary metrics and exploratory data analysis

Of the 232 survey events, at least one BSB individual was counted on 162 surveys, with zero

detections on the remaining 70 surveys. Of the 162 surveys with detections, 34 had fewer than

10 individuals, 83 had 10–99 individuals, and 45 had 100 up to a maximum count of 770

individuals. BSB density ranged from 0.0–1.99 individuals/acre (total DCA area). Mean density

was 0.18 and median density was 0.0 individuals/acre.

Bean plots show that the HSV as calculated in the original HSM formula did not increase with

abundance. In fact there was little difference in HSV at any abundance (Figure 7). Where BSB

are present, there were few locations where HSV = 0.0, rather most HSVs were intermediate

values of 0.2–0.4.

P a g e | 42

Figure 7: Bean plot of Habitat Suitability Values (HSVs) across categories of abundance for BSB (absent = no BSB detected, other number ranges indicate the total count of individuals of BSB for that survey event The thickness of the blobs increase where multiple tick marks overlap at a similar SIV/abundance combination. The thick horizontal line in each vertical bar shows the average HSV for that group of data points, the thin dotted line shows the overall sample mean.

Bean plots for individual HSM parameters show that most of the parameter Suitability Index

Values (SIVs) included in this model did a poor job of distinguishing between high and low

abundance (Figure 7). Salinity SIV appeared to distinguish abundances well, increasing from an

average suitability of 0.5 at locations where BSB were absent to 0.85 at locations with high

abundance. The other parameter SIVs appeared not to distinguish very well if at all between

absent and abundant locations. Water availability and depth both predicted high suitability

across all abundances, whereas island area predicted low suitability.

P a g e | 43

Figure 8: Bean plot of Suitability Index Values (SIVs) across categories of abundance for BSB (absent = no BSB detected, other number ranges indicate the total count of individuals of BSB for that survey event). The thickness of the blobs increase where multiple tick marks overlap at a similar SIV/abundance combinations. The thick horizontal line across each vertical bar shows the average SIV for that group of data points, the thin dotted line shows the overall sample mean.

BSB HSM refinement

Three pathways in the RPART tree (Figure 9) led to high BSB density: 1) island area > 2.4%; 2)

less than 2.4% island area and very low dry area; 3) some dry area, but less than 24%, with

salinity under 108 mS/cm (i.e., anything less than the highest salinity category).

P a g e | 44

Figure 9: The BSB RPART tree shows that island area, dry area, microtopography, and salinity can be used to differentiate high density from low density surveys. Variable importance ranking of the features that differentiate high from low density surveys: island area (33%), dry area (31%), microtopography (27%), water depth (4%), and salinity (4%). Variable abbreviations: “Islands” = island area, units are proportion of DCA dry area on a scale of 0.0-1.0; “DryArea” units are proportion of total DCA area that is not wet on a scale 0.0-1.0; “MTG10cm” = microtopography >10 cm, units are proportion of DCA dry area on a scale 0.0-1.0; “MT3cm” = microtopography 0-3 cm, units are proportion of DCA dry area on a scale 0.0-1.0; salinity units are mS/cm.

Regression models indicate strong associations with salinity, islands, and dry area, with weaker

influence of water depth and microtopography. BSB density is highest at low to moderate

salinity levels and then declines at the highest values. The effect of dry area on BSB density is

P a g e | 45

negative, while density reaches a maximum at intermediate island area (~7%). Removing non-

significant variables improved AIC by 9 points and did not change R2 (Table 10). Adding a DCA

random effect to the mixed model improved R2 and AIC somewhat, indicating that there may

be some unmeasured habitat features that could inform the model and help better predict BSB

abundance.

Table 10: Fit statistics (R2 and AIC) and model parameter coefficients for BSB global regression model, including variables that were included in original HSM or the RPART model, and selected variable model after removing least significant variables until AIC no longer improves. See Appendix (Figures A5, A6, and A7) for selected-variable model plots of salinity, islands, and dry area effects.

Breeding Shorebirds

Global Model (R2=0.36, AIC=-126) Selected Variables (R2=0.36, AIC=-135)

Estimate Std.Error Pr(>|t|) Estimate Std.Error Pr(>|t|)

Intercept 0.12 0.06 0.04 * 0.16 0.04 0.00 ***

DCA acres 0.00 0.00 0.93

DCA acres2 0.08 0.09 0.39

Water depth 0-10 cm

0.34 0.13 0.01 **

Water depth 10-25 cm

-0.21 0.19 0.27 0.30 0.12 0.01 *

Water depth 25-40 cm

-0.65 0.22 0.00 ** -0.28 0.16 0.08 .

Salinity -0.32 0.19 0.10 . -0.63 0.18 0.00 ***

Salinity2 0.55 0.19 0.00 ** -0.32 0.18 0.08 .

Islands -0.73 0.18 0.00 *** 0.55 0.19 0.00 **

Islands2 -0.72 0.35 0.04 * -0.73 0.18 0.00 ***

Dry area 0.47 0.22 0.03 * -0.92 0.25 0.00 ***

Dry area2 0.01 0.21 0.96 0.42 0.20 0.04 *

Veg cover -0.05 0.20 0.82

Veg cover2 -0.41 0.22 0.06 .

Micro-Topography 0-3 cm

0.18 0.12 0.14 -0.35 0.19 0.07 .

Micro- Topography 3-10 cm

-0.03 0.03 0.35 0.18 0.12 0.12

Year 2013 -0.05 0.03 0.08 . -0.03 0.03 0.29

Year 2014 0.12 0.06 0.04 * -0.05 0.03 0.08 .

Mixed model including DCA random effect: (R2=0.58, AIC=-141)

P a g e | 46

Based on the support for water depth variables, salinity, dry area, microtopography, and island

area in the RPART and regression model results, we identified the following changes for our

preliminary refined HSM: increased suitability of 10–25 cm water; and increased suitability of

the highest salinity categories, while decreasing suitability of the lowest category slightly (Table

11). We restructured the HSV formula to reflect the related effect of water depth and

availability, and combined the variables that appear to have smaller effect on BSB density as an

arithmetic mean (islands, microtopography, dry area, and vegetation extent). Islands are

recognized as being important to breeding shorebirds for avoiding land predators and so were

parameterized to penalize their absence. We found little support for associations with seasonal

water availability and vegetation cover, so these variables were parameterized based on expert

opinion. Since Snowy Plovers use open habitat, vegetation cover over 10% was penalized.

Table 11: Comparison of original Habitat Suitability model parameters for Breeding Shorebird to those of the refined model

Parameters Habitat Value

Measurements

Original BSB Habitat Suitability Model

Suitability Index Value (SIV)

New BSB Habitat Suitability Model

Suitability Index Value (SIV)

Water depth (proportion of total water >0

cm deep)

0cm (no water) 0-10 cm

10-25 cm 25-40 cm > 40 cm

0.01 * proportion of cell 1.0 * proportion of cell 0.4 * proportion of cell

0.05 * proportion of cell 0.0

[sum total*2]

0.0 1.0 1.0 0.1 0.1

[sum total]

Salinity (average for

polygon)

0-5 mS/cm 5-15 mS/cm

15-50 mS/cm 50-70 mS/cm

70-100 mS/cm >100 mS/cm

0.8 1.0 1.0 0.5 0.1 0.0

0.5 1.0 1.0 1.0 0.6 0.3

Seasonal Water

Availability (monthly)

Spring (February, March, April)

Summer (May, June, July)

Fall (August, September, October)

0.5

0.5 0.0

0.0

January 0.0 February 0.0 March 0.1 April 0.2 May 0.2 June 0.2

P a g e | 47

Winter (November, December, January)

[sum total] July 0.2 August 0.1 September 0.0 October 0.0 November 0.0 December 0.0

Island area (% of total DCA

area)

<4% For each additional

1% add 0.1 to a maximum of 1 at 10%

and above

0.3

1.0

0% 0.0 1% 0.1 2% 0.2 3% 0.3 4% 0.4 5% 0.5 6% 0.6 7% 0.7 8% 0.8 9% 0.9

10% and above 1.0

Dry Area

0-10% 10-20% 20-30% 30-60% 60-80%

80-100%

0.1 0.3 0.6 1

0.7 0.4

0-3% 0.1 6% 0.2 9% 0.3

… 30% 1.0

30-40% 0.8 40-80% 0.4

80-100% 0.1

Micro-topographic relief of dry

areas

>20 cm (tillage) 5-20 cm 2-5 cm 0-2 cm

0.7 1.0 0.7 0.2

> 10 cm 0.8 3-10 cm 1.0 0-3 cm 0.3

Vegetated extent (% of cell cover by any veg type)

>50% 25-50% 10-25% 0-10%

0.3 0.4 0.6 1.0

0.1 0.2 0.5 1.0

regression of log abundance to habitat

value:

Habitat Acres R2 = 0.21 Habitat Suitability + Acres R2 = 0.37

Habitat Acres R2 = 0.38 Habitat Suitability + Acres R2 = 0.50

The HSV calculation has been altered from the original HSM to reflect our estimation of relative

strength and logical associations between the parameters: HSV = (Water depth SIV *

WaterAvailability SIV)0.5 * SalinitySIV * (Island Area SIV + MicroTopoSIV + DryAreaSIV +

VegExtentSIV)/4. The new refined HSM parameters improve the fit of log guild abundance to

the habitat assessment parameters. The habitat acres regression improved from R2 = 0.21 to

P a g e | 48

0.38, and habitat suitability + acres improved from 0.37 to 0.50 (Table 11). The range of HSV

values decreased from 0–0.87 in the original HSM to 0–0.61 with the refined HSM parameters.

Migratory Shorebird Guild

Basic summary metrics and exploratory data analysis

Of the 926 survey events, at least one MSB individual was counted on 502 surveys, with zero

detections on the remaining 424 surveys. Of the 502 surveys with detections, 110 had fewer

than 10 individuals, 125 had 10–99 individuals, 186 had 100–999 individuals, and 81 had 1000

up to a maximum count of 11,466 individuals. In examining the abundance data by survey, the

larger DCAs tend to be the ones with the highest counts. There were 58 survey events with

guild abundance counts larger than 1500 individuals, and the average size of those DCAs is 596

acres, nearly double the average cell size. MSB density ranged from 0.0–25.6 individuals/acre

(total DCA area). Mean density was 0.81 and median was 0.01 individuals/acre. Density was

capped at 15 individuals/acre to truncate four outliers.

The HSV bean plot shows that the HSV as calculated in the original HSM formula was higher on

average at locations where MSB were detected than where absent (Figure 10). However there

was little difference in HSVs at low versus high abundances. There were many locations where

MSB were present and HSV = 0.0, as well as locations where MSB were absent and HSV was

relatively high (0.4 or higher).

P a g e | 49

Figure 10: Bean plot of Habitat Suitability Values (HSVs) across categories of abundance for BWF (absent = no BWF detected, other number ranges indicate the total count of individuals of BWF for that survey event The thickness of the blobs increase where multiple tick marks overlap at a similar SIV/abundance combination. The many unique values in the HSV plot result in few blobs being plotted. The thick horizontal line in each vertical bar shows the average HSV for that group of data points, the thin dotted line shows the overall sample mean.

Individual HSM parameter bean plots show that few of the parameter SIVs included in this

model distinguished between high and low abundance (Figure 11). Salinity appeared to

increase from an average suitability of 0.4 at locations where MSB were absent to 0.6 at

locations where MSB were detected, but did not increase with higher abundance. Water

availability and island area SIVs also showed small differences between locations where MSB

were absent versus abundant, though there were mostly high values for water availability and

low values for island area. The other SIV parameters appeared not to distinguish very well if at

all between absent and abundant locations.

P a g e | 50

Figure 11: Bean plot of Suitability Index Values (SIVs) across categories of abundance for MSB (absent = no MSB detected, other number ranges indicate the total count of individuals of MSB for that survey event). The thickness of the blobs increase where multiple tick marks overlap at a similar SIV/abundance combinations. The thick horizontal line across each vertical bar shows the average SIV for that group of data points, the thin dotted line shows the overall sample mean.

MSB HSM refinement

The RPART tree shows that there was one main branch that led to a set of high density nodes

(Figure 12): where there was some water in the 10–25 cm depth class, spring surveys, certain

P a g e | 51

vegetation structure types (the more diverse ones), and then either > 33% dry area or < 33%

dry area and > 3.6% island area.

Figure 12: The MSB RPART tree shows that water depth, season, vegetation structure, island area, and dry area can be used to differentiate high density from low density surveys. Variable importance ranking of the primary features that differentiate high from low density surveys: water depth (30%), vegetation structure (19%), season (15%), dry area (13%), DCA acres (10%), island area (9%), and microtopography (5%). Variable abbreviations: “WatDep25” = proportion of total water area in depths of 10-25 cm on a scale of 0.0-1.0; “VegStruc” = vegetation structure types, as described in LADWP 2011; “Islands” = island area, units are proportion of DCA dry area on a scale of 0.0-1.0; “DryArea” units are proportion of total DCA area that is not wet on a scale 0.0-1.0; Seasons are “Fll” = fall and “Spr” = spring.

The MSB regression results indicate associations with DCA size, water depth, salinity, season,

and vegetation structure. MSB density is positively related to DCA area, water area in the

P a g e | 52

shallow and especially 10–25 cm depth classes, and intermediate salinity. Removing the non-

significant variables from the global regression model changed the model fit only slightly (Table

12). Adding DCA as a random effect in the mixed model also led to minimal improvements in

model fit, indicating that there may be few unmeasured environmental variables that would

improve HSM fit.

Table 12: Fit statistics (R2 and AIC) and model parameter coefficients for MSB global regression model, including variables that were included in original HSM or the RPART model, and selected variable model after removing least significant variables until AIC no longer improves. See appendix (Figures A8, A9, and A10) for selected-variable model plots of salinity, DCA acres, and vegetation cover effects.

Migrating Shorebirds

Global Model (R2=0.26, AIC=1389) Selected Variables (R2=0.24, AIC=1385)

Estimate Std.Error Pr(>|t|) Estimate Std.Error Pr(>|t|)

Intercept 0.29 0.09 0.00 ** 0.26 0.08 0.00 **

DCA acres 2.50 0.70 0.00 *** 2.45 0.66 0.00 ***

DCA acres2 -1.59 0.58 0.01 ** -1.48 0.56 0.01 **

Water depth 0-10 cm

0.16 0.14 0.26 0.20 0.09 0.03 *

Water depth 10-25 cm

1.01 0.20 0.00 *** 0.90 0.16 0.00 ***

Water depth 25-40 cm

-0.20 0.25 0.42

Salinity -1.17 0.68 0.09 . -0.94 0.61 0.12

Salinity2 -2.03 0.56 0.00 *** -1.97 0.54 0.00 ***

Season (spring)

0.25 0.04 0.00 *** 0.23 0.03 0.00 ***

Islands 0.97 0.58 0.09 . 0.01 0.01 0.08 .

Islands2 -0.36 0.54 0.51

Dry area 0.98 1.08 0.36

Dry area2 -1.27 0.64 0.05 *

Veg cover -1.47 0.75 0.05 . -0.01 0.00 0.10

Veg cover2 -0.16 0.75 0.83

Veg (HHSD) 0.09 0.20 0.65 0.04 0.17 0.82

Veg (LGSD) 0.00 0.08 0.97 -0.01 0.07 0.92

Veg (MHLSS) -0.18 0.18 0.30 -0.17 0.17 0.34

Veg (NV) -0.17 0.08 0.04 * -0.18 0.08 0.02 *

Veg (SD) -0.18 0.11 0.09 . -0.21 0.10 0.03 *

Veg (SDAS) -0.14 0.08 0.10 -0.15 0.08 0.07 .

P a g e | 53

Micro-Topography 0-3 cm

-0.39 0.34 0.25 -0.55 0.20 0.01 **

Micro- Topography 3-10 cm

-0.23 0.20 0.25

Year 2013 0.01 0.04 0.76 -0.01 0.04 0.89

Year 2014 -0.08 0.04 0.05 . -0.09 0.04 0.04 *

Mixed model including DCA random effect: (R2=0.39, AIC=1393)

Based on the support for water depth, season, dry area, salinity, and island area in the RPART

and regression model results, we identified the following changes for our refined HSM: slightly

increased suitability of 0 cm water and >10 deep water categories; adjusted seasonal water

availability to reflect importance of early spring and summer water; slightly reduced suitability

of higher vegetation cover (Table 13). Salinity was a rather weak predictor of MSB density so we

increased suitability for the two lowest and three highest salinity levels. We combined the three

weaker predictors, salinity, island area, and vegetation cover, into an arithmetic mean to

reduce the negative consequences of low scores in any of these variables and to emphasize the

importance of the water depth and availability parameters.

Table 13: Comparison of original Habitat Suitability model parameters for Migrating Shorebird to those of the refined model

Parameters Habitat Value

Measurements

Original MSB Habitat Suitability Model

Suitability Index Value (SIV)

New MSB Habitat Suitability Model

Suitability Index Value (SIV)

Water depth

0cm (no water) 0-10 cm

10-25 cm 25-40 cm >40 cm

0.0 * proportion of cell 1.0 * proportion of cell 0.4 * proportion of cell

0.05 * proportion of cell 0.05 * proportion of cell

[sum total*2]

0.2 1.0 1.0 0.2 0.1

[sum total]

Salinity (average for

polygon)

0-5 mS/cm 5-15 mS/cm

15-50 mS/cm 50-70 mS/cm

70-100 mS/cm

0.2 0.8 1.0 0.5 0.1

0.5 1.0 1.0 1.0 0.6

P a g e | 54

>100 mS/cm 0.0 0.1

Seasonal Water

Availability (monthly)

Spring (February, March, April)

Summer (May, June,

July)

Fall (August, September, October)

Winter (November, December, January)

0.4

0.05

0.5

0.05

[sum total]

January 0.025 February 0.1

March 0.15 April 0.2 May 0.05 June 0.025 July 0.1 August 0.15 September 0.1

October 0.05 November 0.025 December 0.025

[sum total]

Island area (% of total DCA area)

<4% For each additional 1%

add 0.1 to a maximum of 1 at 10% and above

0.3

1.0

0% 0.0 1% 0.1 2% 0.2

… 9% 0.9

10% and above 1.0

Total vegetation

cover

> 50% 25-50% 10-25% 0-10%

0.3 0.4 0.6 1.0

0.1 0.25 0.5 1.0

regression of log abundance to habitat

value:

Habitat Acres R2 = 0.23 Habitat Suitability + Acres R2 = 0.33

Habitat Acres R2 = 0.30 Habitat Suitability + Acres R2 = 0.37

The HSV calculation was changed in comparison to the original HSM with the replacement of

vegetated extent SIV with microtopography SIV, and only averaging the SIVs for salinity and

islands: HSV = (Water depth SIV * Water Availability SIV)0.5 * (2*Salinity SIV + Island Area SIV +

Veg Cover SIV)/4. The new refined HSM parameters led to a small improvement in the fit of log

guild abundance to the habitat assessment parameters. The habitat acres regression improved

from R2 =0.23 to 0.30, and habitat suitability + acres improved from R2 =0.33 to 0.37. The range

of HSV values decreased from 0–0.98 in the original HSM to 0–0.80 with the refined HSM

parameters.

P a g e | 55

Diving Water Bird Guild

Basic summary metrics and exploratory data analysis

Of the 922 survey events, 421 had counts of at least one DWB individual and 501 had zero

detections. Of the 421 surveys with detections, 89 had fewer than 10 individuals, 127 had 10–

99 individuals, 178 had 100–999 individuals, and 27 had 1000 up to a maximum count of 7520

individuals.

In examining the abundance data by survey, several very large DCAs tended to be the ones with

the highest counts. There were 14 survey events with guild abundance counts larger than 1500

individuals, and these were primarily on T16 (1056 acres), T18S (1166 acres), and T25S (820

acres). These largest counts also tended to be dominated by either Ruddy Ducks or Eared

Grebes but not large numbers of both species at the same locations, and the difference appears

to be related to salinity – Ruddy Ducks at lower salinity locations than Eared Grebes. DWB

density ranged from 0.0–114.2 individuals/acre (total area of 40+ acre ponds only, not including

11 values capped at 15). Mean density was 0.66 and median is 0.0 individuals/acre. One outlier

was removed where a very high abundance value occurred when water area was recorded as 0

acres.

The HSV bean plot shows that the original HSM formula did reflect high/med/low abundance as

average HSV increased at each abundance level, but there were some discrepancies (Figure 13).

Notably, there were a fair number of high and medium abundance counts at low HSVs, as well

as some moderate and high HSVs with no DWB guild individuals detected.

P a g e | 56

Figure 13: Bean plot of Habitat Suitability Values (HSVs) across categories of DBW abundance (absent = no DWB individuals detected, other number ranges indicate the total count of DWB individuals for that survey event). The thickness of the blobs increase where multiple tick marks overlap at a similar SIV/abundance combination. The many unique values in the HSV plot result in the lack of blobs being plotted. The thick horizontal line in each vertical bar shows the average HSV for that group of data points, and the thin dotted line shows the overall sample mean.

Bean plots for individual HSM parameters show the three Suitability Index Values (SIVs)

included in this model increased with higher abundance (Figure 14). This pattern was perhaps

strongest with the water depth parameter, where average HSV at absent sites was less than 0.2

but at the abundant sites with >100 individuals it was 0.6. There are numerous surveys where

DWB were absent but the water depth HSV was relatively high, as well as some surveys where

DWB were detected but HSV was low or even zero. The water availability SIV at locations where

DWB were detected was almost universally >0.9, whereas at locations where DWB were absent

there were still many data points with high SIVs and only a few with low SIVs. Because of this

lack of range in SIVs this parameter likely did little to help the HSM predictions. For the salinity

parameter, locations where DWB were detected on average had higher SIVs, but there were

many locations with 1–10 DWB individuals that had zero SIV for salinity and many others where

SIV was positive but DWB were absent.

P a g e | 57

Figure 14: Bean plot of Suitability Index Values (SIVs) across categories of abundance for BWF (absent = no BWF detected, other number ranges indicate the total count of individuals of BWF for that survey event). The thickness of the blobs increase where multiple tick marks overlap at a similar SIV/abundance combinations. The many unique values in the water depth SIV plot result in the lack of blobs being plotted. The thick horizontal line across each vertical bar shows the average SIV for that group of data points, the thin dotted line shows the overall sample mean.

DWB HSM refinement

There were multiple paths in the RPART tree that led to high density (Figure 15). The first split

was for August water, when it is present then highest density was reached at locations with

P a g e | 58

Figure 15: The DWB RPART tree shows that water availability, pond area, salinity, pond depth, and year can be used to differentiate high density from low density surveys. Variable importance, a measure of how frequently variables, including surrogates for the ones listed in the tree, reveals that pond acres (25%), water availability (21%), pond water depth (26%), salinity (19%), year (5%), and sheet flow (4%) are the primary features that differentiate high from low density surveys. Variable abbreviations: “AugWat” and “JulWat” = presence of water in August and July, respectively, units are binary, 1 for present and 0 for absent; “PondAcre” = total area of water in >40 acre ponds, units are acres; “PonDep” = relative area of pond water in different depth classes, “G4” = greater than 40 cm, “10” = less than 10 cm, units are proportions; salinity units are mS/cm.

pond area < 55 acres, and where salinity was < 26 mS/cm. When salinity was > 26 mS/cm then

high density was found in a small set of surveys where the proportion of deep water (over 40

P a g e | 59

cm) was > 0.48. A second path to high density existed where no August water was present, but

July water was, and salinity was between 12 and 21 mS/cm.

Regression model results show that there were strong associations with water depth > 40 cm,

and salinity (Table 14). The DWB global regression model contained fewer variables than other

guilds; DWBs being effectively confined to open water makes the inclusion of vegetation,

microtopography, and dry area variables unnecessary. Only two non-significant variables were

removed from the global model, and thus the effect on model fit was small. When DCA was

included as a random effect in the mixed model both R2 and AIC improve strongly, indicating

that, like BWF and BSB, there may be some unmeasured habitat factors that could explain DWB

density.

Table 14: Fit statistics (R2 and AIC) and model parameter coefficients for DWB global regression model, including variables that were included in original HSM or the RPART model, and selected variable model after removing least significant variables until AIC no longer improves. See Appendix (Figure 11) for the selected-variables model plot of the salinity effect.

Diving Water Birds

Global Model (R2=0.15, AIC=1481) Selected Variables (R2=0.15,

AIC=1475)

Estimate Std.Error Pr(>|t|) Estimate Std.Error Pr(>|t|)

Intercept 0.10 0.05 0.02 * 0.12 0.03 0.00 ***

Pond acres -0.43 0.69 0.54

Pond acres2 1.33 0.64 0.04 *

Pond depth 10-25 cm

0.25 0.13 0.05 . 0.16 0.12 0.18

Pond depth 25-40 cm

0.30 0.14 0.04 * 0.22 0.14 0.11

Pond depth >40 cm

0.74 0.08 0.00 *** 0.70 0.07 0.00 ***

Salinity -2.25 0.55 0.00 *** -2.12 0.54 0.00 ***

Salinity2 -1.35 0.55 0.01 * -1.40 0.54 0.01 **

Season (spring)

-0.01 0.04 0.84

Year 2013 -0.01 0.04 0.74

Year 2014 -0.02 0.04 0.61

Mixed model including DCA random effect: (R2=0.40, AIC=1355)

P a g e | 60

Based on support for salinity, pond depth variables, and seasonal water availability in the

RPART and regression model results, we identified the following changes for our preliminary

refined HSM: increased suitability of deep (>40 cm) and shallow (10–25 cm) water; slightly

increased suitability of the lowest and two of the higher salinity categories; adjusted suitability

of monthly water availability to reflect high use periods of spring and fall, as well as summer

months prior to fall migration period (July and August), and added additional value if water

persisted across the entire year.

We also explored the effect of water area and DCA size on DWB density but found no

compelling evidence of effects. This question deserves further attention, as the results of our

analyses were unclear. Previous implementations of the HSM have used DCA area in the

Habitat Acres calculation, however we suggest instead using total area of individual ponds >40

acres in size. This change will significantly lower the total Habitat Acres values in comparison to

previous analyses, but better reflects the total area of habitat available.

The new refined HSM parameters did improve fit of log guild abundance to the habitat

assessment parameters (Table 15). The regression of habitat acres improved from R2 = 0.28 to

0.38, and habitat suitability + acres improved from R2 = 0.47 to 0.51. The HSV calculation is the

same as in the original HSM: HSV = Water depth SIV * (Salinity SIV * Water Availability SIV)0.5.

The range of HSV values changed little, from 0–1.0 using the original HSM, to 0–0.93 with the

refined HSM. However median HSV has increased from 0.08 with the original HSM to 0.23

under the new version.

Table 15: Comparison of original Habitat Suitability model parameters for Diving Waterbirds to those of the refined model

Parameters Habitat Value

Measurements

Original DWB Habitat Suitability Model

Suitability Index Value (SIV)

New DWB Habitat Suitability Model

Suitability Index Value (SIV)

Water depth in ponds

greater than 40 acres

0cm (no water) 0-10 cm

10-25 cm 25-40 cm >40 cm

0 * proportion of pond 0.05 * proportion of pond 0.1 * proportion of pond 1.0 * proportion of pond 0.8 * proportion of pond

0 0.05 0.5 0.8 1.0

P a g e | 61

[sum total] [sum total]

Salinity (average for

polygon)

0-5 mS/cm 5-15 mS/cm

15-50 mS/cm 50-70 mS/cm

70-100 mS/cm 100-150 mS/cm

>150 mS/cm

0.4 0.8 1.0 0.6 0.4 0.0 0.0

0.6 0.8 1.0 0.8 0.4 0.2 0.0

Seasonal Water

Availability (monthly)

Spring (February, March, April)

Summer (May, June,

July)

Fall (August, September, October)

Winter (November, December, January)

0.4

0.05

0.5

0.05

January 0.025

February 0.05 March 0.05 April 0.05

May 0.05 June 0.05 July 0.1

August 0.15 September 0.1 October 0.1

November 0.05

December 0.025

Total possible = 0.8, if total is > 0.75 add 0.2

regression of log abundance to habitat

value:

Habitat Acres R2 = 0.28 Habitat Suitability + Acres R2 = 0.47

Habitat Acres R2 = 0.38 Habitat Suitability + Acres R2 = 0.51

Section 5: Discussion

HSM strategy

Habitat models used in conservation planning serve an important role of streamlining and

simplifying decisions that are best made using data. However, when constructing and

evaluating models it is often necessary to parameterize them with expert-based knowledge to

bridge gaps in evidence for species’ habitat preferences. Expert-based local knowledge can also

P a g e | 62

provide guidance for key goals or targets that would otherwise be ignored by integrative

knowledge acquired from other data sources (Beazley et al. 2010). In our evaluations of HSM

parameters we relied heavily on expert-based knowledge to interpret the results of our

analyses.

It is clear that high counts of the number of individuals in an area of habitat do not necessarily

mean that habitat is optimal for individual fitness or species population viability (Garshelis

2000), and thus our evaluations using survey counts are done in absence of that important

information, especially for breeding guilds. We relied heavily on expert knowledge to help in

this case. Our goal with this approach was to ensure that the final outputs of HSMs, whether

habitat value acres or any other metrics, are as representative and accurate as they can

possibly be so that adaptive management in the future is effective.

There are some assumptions built into the calculations for the Owens Lake HSMs that LADWP

managers and the Habitat Working Group should be cognizant of. The habitat acres calculation,

where HSV is multiplied by the total area of the DCA (or water area in the case of DWB)

assumes a linear non-interacting relationship between area, abundance, and suitability. In

other words one DCA that is twice as large as another DCA with otherwise identical habitat

conditions is assumed to provide twice the habitat value. This may be justified, but deserves

further attention. We attempted to account for area effects as much as possible by evaluating

HSMs with density, thus controlling for area in the dependent variable, and including area on

equal footing with other habitat factors in the evaluation models. We found very few significant

effects of area, which implies that the interaction between suitability and area may be minimal.

Specifically we evaluated whether there was any evidence of a decline in guild density with

increasing DCA size or water area and found no such patterns. Where we did find evidence of

an area effect it was a decrease in density for small, relative to large, DCA areas, and we built

that effect in to the HSM.

The final HSV calculation that integrates habitat value across the entire year needs further

attention as well. Of various options, one method is to combine multiple seasonal (May and

November) habitat measurements by first averaging the values across seasons and then

P a g e | 63

calculating SIVs and HSV from those averaged values. Another is to calculate each SIV for each

season, then average the SIVs and combine into a single HSV. We suggest the optimal method is

to calculate two HSVs, one for each season, and then average those HSVs. This method would

apply only to the migratory guilds, for which habitat needs include two seasons, whereas for

breeding guilds only a single set of habitat measurements in May is necessary. The two HSVs

can be evaluated separately if inference on seasonal habitat availability might be useful. Finally,

the method of averaging the seasonal HSVs should be assessed. We suggest that each season

be equally weighted in the calculation of yearly HSV. Note that we have already created some

weighting within the HSV by targeting certain months in the water availability SIV. We

considered weighting the seasonal HSVs by total guild abundance across all surveys in each

season knowing that for some guilds abundance varies widely by season. Without substantial

evidence to suggest that one season is more valuable to the guild from the perspective of a

species’ population viability or conservation value, we suggest keeping them weighted equally

until this question can be addressed more thoroughly. It is not clear that counts alone are a

good indicator of overall seasonal value of habitat for the different guilds.

For example, migratory shorebird counts are considerably higher at Owens Lake in spring than

fall survey periods, but it is not clear which season is more valuable to population viability of

those birds. Northbound migration in spring is compressed over a period of about 1.5 months,

with stopover periods of individuals of about 7 days (Skagen et al. 2008). By contrast, fall

migration is much more leisurely, spanning about 4 months. In fall adults migrate first, followed

by juveniles, and there are more total birds migrating in fall as a consequence of reproduction.

Timing of water availability at Owens Lake in the fall does not overlap well with shorebird

migration, which begins by late June with many birds moving in July and August (especially

Wilson’s Phalarope, a saline-adapted species) when there is limited water. Thus the lower

counts in fall may result from less available water during the peak migration periods and birds

being spread out over a longer period of time. Consequently, it remains unclear whether

shorebird habitat is more important in one season over the other.

P a g e | 64

Migratory waterfowl counts were higher on fall than spring surveys. In fall they were especially

dominated by one species, the Northern Shoveler. If the focus of migratory waterfowl habitat

management at Owens Lake was targeted towards that species then weighting the fall period

higher may be justified. However, abundances of other species in this guild are largely similar

between spring and fall so we feel it is unjustified to unequally weight habitat suitability

calculations between seasons based on the abundance of only one species. Because of issues

such as these we did not believe there was compelling evidence that existing counts of birds

were a good indicator of the value of habitat by season for any of the guilds. In our opinion the

conservative approach is to keep seasons weighted equally until robust data are available to

justify a different approach.

If additional seasonal measurements of habitat at the Owens lakebed are available in the future

we recommend that they be incorporated into HSV calculations following the logic we have laid

out above. For example, if August aerial imagery and habitat measurements become available,

those data can be used to calculate August SIVs, potentially for each guild. In the case of the

breeding guilds, BWF and BSB, as well as DWB August represents a time where few individuals

are present on the lake and thus August SIVs are not useful for assessing habitat. For the

migratory guilds however, there are significant numbers of both MWF and MSB individuals

present in August and thus an August HSV would be informative for assessing habitat. Following

our suggestion above regarding a lack of robust data to assess the relative habitat value across

seasons for the migratory guilds, we feel that weighting the May, August, and November

habitat assessments equally into a three-part HSV would be the best approach.

Caveats on our evaluations of the HSMs

Model fit for guilds was moderate, with R2 values of regressions between Habitat Acres and log

guild abundances of 0.17 (BWF), 0.23 (MWF), 0.35 (BSB), 0.25 (MSB), and 0.36 (DWB). Several

factors have likely reduced our ability to predict habitat use at Owens Lake. The temporal

disconnect between bird surveys and when certain habitat parameters were measured

certainly has had some influence. We dropped or truncated counts where there was high bird

counts and no water, but less obvious mismatches were included in the modeling and

undoubtedly influenced model fit. The resolution at which habitat data and bird data are

P a g e | 65

collected may also affect model fit. For example, the salinity measurements averaged across

the entire cell may not be representative of the particular areas of the cell that plovers and

other shorebirds were using because salinity may vary within a cell. Many cells have distinct

areas where fresh water is applied, then flows and pools in other areas, resulting in wide

variation in salinity levels within some DCAs. Counting birds and habitat parameters at finer

scales may improve model fit, but it is not clear whether improved model fit would be worth

the added cost of collecting finer resolution data.

For breeding guilds the counts used to evaluate the HSMs are primarily of foraging birds and

likely not as precise a measure of nesting habitat as brood counts or nest counts would be.

Actual nesting often occurred outside of the DCAs where breeding guild individuals were

counted. Nest counts may be a more fruitful or supplemental approach to identify breeding

habitat value for members of these two guilds. The breeding guilds and DWB had much better

model fit when DCA was added as a random effect, which indicates that there are unmeasured

features of those locations that could explain guild abundance. Some possibilities include

conditions in neighboring cells and other landscape features; variations in invertebrate

productivity that may not be proportional to salinity measurements; proximity to roads or

other disturbances; presence of predators; and soil or substrate conditions. Without further

work it will remain unclear what other factors might be important.

Invertebrate productivity is likely a central factor driving guild abundance at Owens Lake,

however we have little information to explore that assumption directly. We fit models to

observed bird density, without regard to their behavior, so there may be some additional

information that could be incorporated and used to improve HSMs in the future. Specifically, it

would be valuable to concurrently monitor the abundance and compositions of invertebrates,

salinity, bird abundance, and bird behavior (foraging vs. loafing, bathing, etc.) to gain insight

into the relationship of these factors to enable managing for maximum foraging opportunities

and bird abundance under the most efficient water use scenarios. For example, a greater

understanding of the relationship between prey availability, temporal and spatial variation, and

P a g e | 66

associated bird foraging and roosting behavior across DCAs would increase our understanding

of habitat value created through habitat management at Owens Lake.

Conservation value of Owens Lake

Is it justified to treat all guilds as representing equal conservation value, or should they be

weighted by regional importance to guide the creation and management of habitats? If these

guilds are equally weighted and their habitat requirements include disparate conditions (as our

results suggest), it will inevitably lead to increasing habitat for one species or group at the

expense of compromising habitat for another. We acknowledge that it can be difficult to

manage for regional importance given that there may be differing opinions on how to assess

importance. However, we feel that an appropriate goal is to aim towards restoring as much of

the historical value as possible (SERISPWG 2004). Its historical value to waterbirds is imperfectly

known, but it is clear that this was a saline lake system that supported large populations of salt

tolerant species typical of other terminal lakes, and thus it would be valuable to manage the

flooded areas of the Owens lakebed to support a system that has as many features as possible

of the former saline lake ecosystem before it was altered by water diversions (Grinnell 1911–

1926, Herbst and Prather 2014). Given the constraints of the soils and climate it seems prudent

to continue managing the lake so that salt-tolerant species are the dominant members of most

or all of the guilds, while also providing conditions that are currently limiting (e.g., water in July

and August) that may benefit salt-tolerant species and others alike.

The BSB guild should be given highest priority for management because the two main members

are both salt-tolerant species, and, of the two, the Snowy Plover is a California Bird Species of

Special Concern (Shuford et al. 2008) and remains a focal species for conservation at Owens

Lake that is surveyed periodically to fulfill mitigation monitoring requirements (GBUAPCD

2008). The DWB, MWF, and MSB guilds should also be given relatively high priority because

their species abundance is dominated by one or a few salt-tolerant species. We recommend

that the BWF guild be given lowest management priority because all of its members generally

prefer fresh water, and studies at Mono Lake indicate the value of saline lakes for breeding

ducks is low, because of the harsh water chemistry, limited fresh water for drinking and

marshes for feeding, and apparent low survival among adults (Jehl 2005).

P a g e | 67

Recommendations

Increasing the availability of water, or managing its depths, at certain periods of the year shows

great promise for improving habitat conditions for multiple guilds. In particular, it would be

valuable to increase shallow flooding in July and August. This would provide shallow water for

migrating shorebirds at a time of year when such habitat is most limited on the landscape

throughout California and the West. This could also benefit the two breeding guilds, either by

extending the breeding season for some species or providing habitat for juveniles before they

migrate or birds of any age dispersing locally or regionally before migrating. Having water in

July and August would increase the seasonal persistence of water and thereby would likely

boost the production of invertebrates used by other guilds that begin migrating later in the fall.

If water depths were subsequently increased in some DCAs toward the end of the fall migration

season for shorebirds this would increase the value of the DCAs to MWF and DWB that migrate

or arrive later in the season. Thus, we believe efforts to incorporate dynamic water

management, specifically those that increase persistence of water in the dry season (July and

August), are prudent. Increased efforts to manipulate salinity, particularly in the draw down

and dry season, may be an important tool to increase habitat suitability while reducing total

water use for many of the guilds. High salinity appears to be a limiting factor during these

periods that reduces habitat suitability. Invertebrate densities are very much related to salinity

and are likely a driver of bird use.

Improvements could be made in the monitoring of both habitat conditions and birds. The

monitoring of habitat information should be closely timed to the bird surveys. If it is too costly

to do this with remote sensing, observers should gather coarser DCA-specific habitat

information in the field on, or within a few days of, the bird surveys. Greater alignment of

habitat and bird data would make it possible to improve management activities in an adaptive

framework. A well described protocol documenting the standardized procedure for conducting

surveys would be useful. Establishing consistent criteria and documentation for how to count

flushed birds, time limits, time of day, and type of behavior (roosting, foraging, on nest) would

allow for inclusion of these effects in models and could account for some of the unexplained

variance.

P a g e | 68

Structured decision making

The HSM provides information necessary for assessing habitat value on Owens Lake during

shifts in operations and application of water. If operations are adjusted to minimize the overall

use of water it may be possible to maintain habitat value. However, the current infrastructure,

built with only dust control requirements in mind, makes it difficult to meet the goals of water

use reduction, dust control, and maintaining bird habitat value. The existing habitat value in a

given set of DCAs can be enhanced by redesigning and incorporating specific habitat features

identified with the HSM. For example, if a DCA is limited by less than optimal salinity for a

guild(s) there currently are few existing options to change salinity without investing in

additional infrastructure. Yet, as part of redesigning a DCA, the infrastructure necessary for

salinity management could be incorporated, along with potentially numerous other habitat

improvements. Therefore, through redesign, habitat parameters can be optimized for a

particular guild in a given DCA. Whereas in other DCAs waterless dust control may be

implemented without an overall habitat loss for the guild.

LADWP should take steps to establish and document transparent methods for optimizing

habitat value across all DCAs throughout the lake including plans for when certain areas are

offline or redesigned. Given the incomplete knowledge of bird habitat relationships at Owens

Lake, the construction of any new or redesigned DCAs should be monitored carefully, and

additional changes in design or operation should be made if the habitat value objectives are not

met by the initial design or management plan. There will need to be some flexibility in this

regard, as any new project will be experimental in nature and may require subsequent

modification to meet its goals. The Habitat Working Group should establish an adaptive

management framework for evaluating both implemented and planned activities on a regular

interval at Owens Lake. The larger challenge into the future will be to collectively manage all of

the various DCAs to provide habitat value that is more than the sum of the parts.

P a g e | 69

Literature Cited

Ackerman, J. T., C. A. Hartman, M. P. Herzog, J. Y. Takekawa, J. A. Robinson, L. W. Oring, J. P.

Skorupa, and R. Boettcher. 2013. American Avocet (Recurvirostra americana), in The Birds of

North America Online (A. Poole, ed.). Ithaca: Cornell Lab of Ornithology. Available from:

http://bna.birds.cornell.edu/bna/species/275 doi:10.2173/bna.275

Bates, D., M. Maechler, B. Bolker, and S. Walker. 2015. Fitting Linear Mixed-Effects Models

Using lme4. Journal of Statistical Software 67(1):1–48.

Beazley, K. F., E. D. Baldwin, and C. Reining. 2010. Chapter 11: Integrating Expert Judgment into

Systematic Ecoregional Conservation Planning. In: Landscape-scale Conservation Planning, S. C.

Trombulak and R. F. Baldwin(eds.). Springer, New York. Pp 235–256.Belrose, Frank C. et al.

1980. Ducks Geese and Swans of North America. Published by Stackpole Books, Harrisburg, PA.

Brua, R. B. 2002. Ruddy Duck (Oxyura jamaicensis), in The Birds of North America Online (A.

Poole, ed.). Ithaca: Cornell Lab of Ornithology. Available from:

http://bna.birds.cornell.edu/bna/species/696 doi:10.2173/bna.696

Cullen, S. A., J. R. Jehl Jr., and G. L. Nuechterlein. 1999. Eared Grebe (Podiceps nigricollis), in The

Birds of North America Online (A. Poole, ed.). Ithaca: Cornell Lab of Ornithology. Available from:

http://bna.birds.cornell.edu/bna/species/433 doi:10.2173/bna.433

Dubowy, P. J. 1996. Northern Shoveler (Anas clypeata), in The Birds of North America Online (A.

Poole, ed.). Ithaca: Cornell Lab of Ornithology. Available from:

http://bna.birds.cornell.edu/bna/species/217 doi:10.2173/bna.217

Gaines, D. 1988. Birds of Yosemite and the East Slope. 2nd edition (revised 1992). Artemisia

Press, Lee Vining California.

Cooper, D. S. 2004. Important Bird Areas of California. Audubon California, Los Angeles.

Fox, J. 2003. Effect Displays in R for Generalised Linear Models. Journal of Statistical Software

8(15):1–27. URL http://www.jstatsoft.org/v08/i15/

P a g e | 70

Great Basin Unified Air Pollution Control District (GBUAPCD). 2008. Appendix D: Mitigation

Monitoring Program (Prepared by Sapphos Environmental, Pasadena California), in 2008 Owens

Valley PM10 Planning Area Demonstration of Attainment State Implementation Plan. Prepared

by the Great Basin Unified Air Pollution Control District, Bishop, California. Available from:

www.gbuapcd.org/ovpm10sip.htm

Great Basin Unified Air Pollution Control District (GBUAPCD). 2016. 2016 Owens Valley Planning

Area PM10 State Implementation Plan. Draft Final. Prepared by Ramboll Environ, Los Angeles,

California. Available from: www.gbuapcd.org/ovpm10sip.htm

Grinnell, J. 1911–1926. Field notes on file at the Museum of Vertebrate Zoology, University of

California, Berkeley. Available at http://mvz.berkeley.edu/FieldnotePhotoMap_Collection.html

Heiberger, R.M. 2016. HH: Statistical Analysis and Data Display: Heiberger and Holland. R

package version 3.1–25. URL http://CRAN.R-project.org/package=HH

Henderson, R. P., and G. W. Page. 1981. III: San Francisco Bay and the interior, in The breeding

status of the Snowy Plover in California (G. W. Page and L. E. Stenzel, eds.), pp. 16–23. Western

Birds 12:1–40.

Herbst, D. B. 1996. Aquatic invertebrate inventory of the Owens Lake Basin. Final report to

Sapphos Environmental. Available from David B. Herbst, Ph.D., Sierra Nevada Aquatic Research

Laboratory, University of California, Mammoth Lakes, CA 93546.

Herbst, D. B. 2001. An overview of information on aquatic invertebrate life of the Owens Lake

Basin and evaluation of habitat suitability of irrigation drainwater. Report to the Great Basin

Unified Air Pollution Control District, Bishop, California. Available from David B. Herbst, Ph.D.,

Sierra Nevada Aquatic Research Laboratory, University of California, Mammoth Lakes, CA

93546.

Herbst, D. B. 2003. Owens Lake playa floodwaters invertebrate survey: Winter and spring 2002–

2003. Submitted to Sapphos Environmental for preparation of 2003 Revised State

Implementation Plan (SIP) EIR (for dust control on the Owens Lake playa). Available from David

P a g e | 71

B. Herbst, Ph.D., Sierra Nevada Aquatic Research Laboratory, University of California, Mammoth

Lakes, CA 93546.

Herbst, D. B., and M. Prather. 2014. Owens Lake – from dustbowl to mosaic of salt water

habitats. Lakeline 34(4): 34–38.

Jehl, J. R., Jr. 1988. Biology of the Eared Grebe and Wilson’s Phalarope in the nonbreeding

season: A study of adaptations to saline lakes. Studies in Avian Biology 12.

Jehl, J. R., Jr. 1994. Changes in saline and alkaline lake avifaunas in western North America in

the past 150 years. Studies Avian Biology 15:258–272.

Jehl, J. R., Jr. 1996. Mass mortality events of Eared Grebes in North America. J. Field Ornithology

67:471–476.

Jehl, J. R., Jr. 2005. Gadwall biology in a hypersaline environment: Is high productivity offset by

postbreeding mortality? Waterbirds 28(3):335–343.

Kampstra, P. 2008. Beanplot: A Boxplot Alternative for Visual Comparison of Distributions.

Journal of Statistical Software, Code Snippets 28(1):1–9. URL http://www.jstatsoft.org/v28/c01/

Los Angeles Department of Water and Power (LADWP). 2003–2015. Mono Lake waterfowl

population monitoring: various annual reports, in annual compliance reports of the Los Angeles

Department of Water and Power to the California State Water Resources Control Board

(SWRCB) in response to SWRCB Order Nos. 98–05 and 98–07. Available from:

www.monobasinresearch.org/onlinereports/index.php#LEGAL

Los Angeles Department of Water and Power (LADWP). 2011. Habitat suitability models for

species guilds that occur on Owens Lake. Los Angeles Dept. of Water and Power, 300 Mandich

St., Bishop, CA 93514.

Los Angeles Department of Water and Power (LADWP). 2013. Owens Lake Master Project:

Transitioning to a waterless and waterwise solutions. Prepared by Nūvis Landscape Architecture

and Planning. Available at www.ladwp.com/docs/OPLADWP050832

P a g e | 72

Los Angeles Department of Water and Power (LADWP). 2014a. Owens Lake dust control

project: 2012–2014 lakewide avian surveys report. Prepared by Debbie House and Sarah

Bryson. Los Angeles Dept. of Water and Power, 300 Mandich St., Bishop, CA 93514.

Los Angeles Department of Water and Power (LADWP). 2014b. Owens Lake dust control

project—2014 Snowy Plover report. Prepared by Bill Deane. Los Angeles Dept. of Water and

Power, 300 Mandich St., Bishop, CA 93514.

Los Angeles Department of Water and Power (LADWP). 2015. Habitat analysis using Habitat

Suitability Models for species guilds that occur on Owens Lake, Inyo County, CA 2012–2014.

Prepared by Los Angeles Dept. of Water and Power. 300 Mandich St., Bishop, CA 93514.

Page, G. W., L. E. Stenzel, J. S. Warriner, J. C. Warriner, and P. W. Paton. 2009. Snowy Plover

(Charadrius nivosus), in The Birds of North America Online (A. Poole, ed.). Ithaca: Cornell Lab of

Ornithology. Available from: http://bna.birds.cornell.edu/bna/species/154

doi:10.2173/bna.154

R Core Team (2014). R: A language and environment for statistical computing. R Foundation for

Statistical Computing, Vienna, Austria. Available from: http://www.R-project.org/

Ruhlen, T. D., C. Laczek-Johnson, and G. W. Page. 2003. Snowy Plovers, American Avocets, and

migrating waterbirds at Owens Lake in 2003. Report of PRBO Conservation Science to CH2M

HILL, 3 Hutton Drive, Suite 200, Santa Ana, CA 92707.

Ruhlen, T. D., G. W. Page, and L. E. Stenzel. 2006. Effects of a changing environment on nesting

Snowy Plovers at Owens Lake, California. Western Birds 37:126–138.

Society for Ecological Restoration International Science & Policy Working Group (SERISPWG).

2004. The SER International Primer on Ecological Restoration. Version 2. Tucson: Society for

Ecological Restoration International. Available from: http://ser.org/resources/resources-detail-

view/ser-international-primer-on-ecological-restoration

P a g e | 73

Shuford, W. D., G. W. Page, and L. E. Stenzel. 2002. Patterns of distribution and abundance of

migratory shorebirds in the Intermountain West of the United States. Western Birds 33:134–

174.

Shuford, W. D., S. Abbott, and T. D. Ruhlen. 2008. Snowy Plover (Charadrius alexandrinus), in

California Bird Species of Special Concern: A ranked assessment of species, subspecies, and

distinct populations of birds of immediate conservation concern in California (W. D. Shuford

and T. Gardali, eds.), pp. 173–179. Studies of Western Birds 1. W. Field Ornithol., Camarillo, and

Calif. Dept. Fish and Game, Sacramento.

Skagen, S. K., D. A. Granfors, and C. P. Melcher. 2008. On determining the significance of

ephemeral continental wetlands to North American migratory shorebirds. Auk 125:20–29. doi:

http://dx.doi.org/10.1525/auk.2008.125.1.20

Therneau, T., B. Atkinson, and B. Ripley. 2015. rpart: Recursive Partitioning and Regression

Trees. R package version 4.1–10. Available from: http://CRAN.R-project.org/package=rpart

Venables, W.N., and B.D. Ripley. 2002. Modern Applied Statistics with S. Fourth edition.

Springer, New York.

Wickham, H. 2007. Reshaping Data with the reshape Package. Journal of Statistical Software

21(12):1–20. Available from: http://www.jstatsoft.org/v21/i12/

P a g e | 74

Appendix

Figure A1: BWF salinity effect.

Figure A2: BWF vegetation cover effect.

P a g e | 75

Figure A3: MWF salinity effect.

Figure A4: MWF water acres effect.

P a g e | 76

Figure A5: BSB salinity effect.

Figure A6: BSB islands effect.

P a g e | 77

Figure A7: BSB dry area effect.

Figure A8: MSB salinity effect.

P a g e | 78

Figure A9: MSB DCA acres effect.

Figure A10: MSB vegetation cover effect.

P a g e | 79

Figure A11: DWB salinity effect.