United States

Department

of Agriculture

Forest Service

Rocky Mountain

Research Station

General Technical

Report RMRS-GTR-99

September 2002

Stopover Ecology of

Landbirds Migrating Along

the Middle Rio Grande in

Spring and Fall

Wang Yong

Deborah M. Finch
Abstract __________________________________________________

Yong, Wang; Finch, Deborah M. 2002. Stopover ecology of landbirds migrating along the middle Rio Grande in

spring and fall. Gen. Tech. Rep. RMRS-GTR-99. Ogden, UT: U.S. Department of Agriculture, Forest Service, Rocky

Mountain Research Station. 52 p.

This research represents the first comprehensive summary of our study of stopover ecology of migratory landbirds

in riparian habitats along the middle Rio Grande of central New Mexico. We report results from mist-netting operations

conducted during spring and fall migration in 1994, 1995, and 1996. A total of 23,800 individuals of 146 species were

captured during the study. Of the 146 species, 53 percent were Neotropical migrants, and 32 percent were temperate

migrants. The most abundant species were the MacGillivray’s Warbler, Wilson’s Warbler, and White-crowned Sparrow

in spring and the Chipping Sparrow, Pine Siskin, and White-crowned Sparrow in fall. Migrants were most abundant

between late April and the first 2 weeks of May in spring and between the last week of August and mid-October.

Temperate migrants passed through the sites earlier in spring and later in fall than Neotropical migrants. About 50

percent of the birds had no visible fat stores upon capture. More birds used riparian habitat along the middle Rio Grande

for stopover in fall than in spring. Species richness and relative abundance were lower in spring (108 species, 4,673

birds) than in fall (125 species, 19,127 birds). In addition to the influx of hatching-year birds in fall, differential use of

migratory routes by landbirds in spring and fall may explain the seasonal difference in capture rate. We recaptured 2,875

birds (12 percent of the total captures) after the day of initial capture. Energetic condition and migratory status affected

recapture probability: birds with low fat stores were more likely to stay overnight, and Neotropical species had shorter

stopovers than temperate migrants and residents. The average rate of body mass gain across all species was 7.46 ±

0.10 percent/day in spring and was 4.68 ± 0.05 percent/day in fall, suggesting that migrants were not only able to gain

energy for regular metabolic needs, but also for accumulating fat stores for migration. The capture rate was highest in

willow habitat (288 birds/1,000 net hour) in spring and in agricultural field/edge habitat (718 birds/1,000 net hour) in fall.

Cottonwood with Russian olive understory had the highest species richness in spring (80 species) and in fall (94

species). Saltcedar habitat had lowest species richness both in spring (26 species) and in fall (33 species). Detrended

correspondence analysis of spring migration data separated species with high abundance in saltcedar from species with

high capture rates in cottonwood overstory habitats, and in agriculture and willow habitats. The same analysis for fall

data separated species with high capture rates in habitats dominated by agriculture, willow, saltcedar, and cottonwood.

Species composition was most similar between agricultural habitat and cottonwood/Russian olive habitat in spring and

among habitats with cottonwood as overstory in fall. During fall migration, about 60 percent of the birds captured were

immatures. Body masses of adult birds were generally higher than those of immature birds, and young birds were more

likely to be fat-depleted when they captured at our sites. The rate of mass gain was similar between immatures and

adults. Our study confirms that riparian habitats along the middle Rio Grande of central New Mexico are important

stopover sites during spring and fall migration for birds that breed in New Mexico, its adjacent States, and at a much

larger geographic scale. Habitat loss and disturbance along the middle Rio Grande could affect not only local breeding

landbird populations, but also many Neotropical and temperate migratory individuals that may use the area for only a

few days each migration season.

Keywords: Neotropical migrants, Rio Grande, bird migration, riparian, New Mexico, tamarisk

Acknowledgments _________________________________________

We are grateful to F. Moore for his guidance and suggestions during all stages of this work. We thank J. Taylor, P.

Norton, K. Brown, and the staff of the Bosque del Apache National Wildlife Refuge and the Rio Grande Nature Center

for allowing access, housing, and assistance for data collection. Members of Rio Grande Bird Research, Inc. helped

in mist-net operations. J. Hubbard, C. Hundertmark, and C. Hunter provided valuable information for establishing

banding sites. G. Bodner, H. Bingham, and D. Hawksworth assisted with data entry. For excellent database

management, we extend a special thanks to M. D. Means. We appreciate data collection efforts by G. Bodner, M.

Connolly, N. Cox, S. Cox, L. Delay, D. Hawksworth, M. Mahrt, C. Mandras, M. D. Means, K. Ryder, J. Sevigney, M.

Sevigney, G. Trujillo, L. Vento, R. Wolbert, and R. Young. We thank J. Kelly and M. Means for reviewing the manuscript.

We thank L. Kingsbury, N. Chadwick, and L. Collins for publishing assistance. Rhode Island Agricultural Experiment

Station and the Department of Natural Resource of the University of Rhode Island and the Department of Plant and Soil

Science of the Alabama A & M University provided partial support to W. Y. during the preparation of this report.

Cover photos: Top right photo: a Neotropical long-distance migrant, the Wilson’s

Warbler (Wilsonia pusilla). Middle left photo: A temperate, short-distance migrant,

the Song Sparrow (Melospiza melodia). Bottom right photo: Assistant Mike Means

removing a migratory bird from a mist net. Photo credits: RMRS staff.
Stopover Ecology of Landbirds

Migrating Along the Middle Rio

Grande in Spring and Fall

Wang Yong

Associate Professor at the Center for Forestry and Ecology

Alabama A&M University

Normal, AL 35762.

Email: ywang01@aamu.edu

Deborah M. Finch

Project Leader, Ecology, Diversity, and Sustainability

of Soil, Plant, Animal, and Human Resources of the

Rio Grande Research Work Unit,

USDA Forest Service, Rocky Mountain Research Station

Forestry Sciences Laboratory

333 Broadway SE

Albuquerque, NM 87102-3497.

E-mail: dfinch@fs.fed.us

Contents_______________________________________

Page

Introduction .............................................................................................................. 1

Methods ................................................................................................................... 2

Study Site .......................................................................................................... 2

Capture and Recapture ..................................................................................... 2

Data Analysis..................................................................................................... 3

Results ..................................................................................................................... 7

Species Composition and Relative Abundance ................................................. 7

Migration Timing .............................................................................................. 11

Fat Stores and Body Mass .............................................................................. 16

Recapture Rate, Body Condition Change, and Length of Stay ....................... 19

Habitat Use ...................................................................................................... 22

Age-Related Migration ..................................................................................... 29

Discussion .............................................................................................................. 32

Species Composition and Relative Abundance ............................................... 32

Migration Route ............................................................................................... 37

Migration Timing .............................................................................................. 37

Fat Stores, Body-Mass Change, and Stopover Length ................................... 38

Habitat Use ...................................................................................................... 39

Age-Related Differences in Capture Rates ..................................................... 40

Site Differences in Capture Rates ................................................................... 41

Conservation Implications ............................................................................... 42

References ............................................................................................................. 42

Appendix I—Common and Scientific Names and Alpha Codes............................. 46

Appendix II—Species Captured by Mist Netting .................................................... 50

Appendix III—Species Detected Over 3-Year Study.............................................. 51

USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002 1

Introduction ____________________

The Rio Grande, one of the longest rivers in North

America (approximately 3,000 km), originates in Southern Rocky Mountains of Colorado, flows the whole

length of New Mexico and forms the entire border

between Texas and Mexico. It is the second greatest

source of permanent water in the desert Southwest,

behind the Colorado River, and supports the largest

cottonwood (Populus fremontii) forest in North

America. However, riparian habitats along the Rio

Grande are under increasing pressure from human

activities. Water management, catastrophic fires, recreation, urban development, and invasion of exotic

plant species such as saltcedar (Tamarix spp.) and

Russian olive (Elaeagnus angustifolia) have altered

riparian habitats, potentially influencing wildlife use

(Finch and others 1995). Knopf (1988) estimated that

more than 90 percent of the original desert riparian

habitat in the Western United States has been eliminated by flood control and irrigation projects.

Because of its south-north orientation and abundant, diverse riparian habitats, the Rio Grande may

serve as a valuable migratory route for many songbird

migrants that breed in North America and winter in

more tropical areas in Mexico, Central America, and

South America (Keast and Morton 1980), and for

species that migrate within the North American biogeographic realm. Because the vegetation along the

riverbank is typically narrow and surrounded by arid

and harsh environment, the riparian habitat along the

Rio Grande in New Mexico may represent a bottleneck

during migration. Many North American songbird

species and individuals funnel into these limited habitats on their journeys to the breeding grounds in

spring or to the wintering ground in fall (Finch and

Yong 2000, Yong and Finch 1996, 1997a). The availability of food, water, cover, and suitable north-south

routing along the river may be critically important and

strongly influential in directing migration of landbirds

(Finch and Yong 2000, Ligon 1961, Stevens and others

1977, Wauer 1977, Yong and Finch 1996).

Stopover Ecology of Landbirds

Migrating Along the Middle Rio

Grande in Spring and Fall

Wang Yong

Deborah M. Finch

The conservation of Neotropical migratory songbirds has been the subject of considerable interest

because some species have declined in recent years

(Askins and others 1990, Martin and Finch 1995,

Moore 2000). The population status of migratory

landbirds in Western North America is less well documented than that in the East. However, recent studies

based on Breeding Bird Survey (BBS) (Robbins and

others 1986, Sauer and Droege 1992, Peterjohn and

Sauer 1993), counts of migrating birds (Pyle and

others 1994), and historic data (Marshall 1988, DeSante

and George 1994) indicate that populations of some

western songbird migrants have also declined. Herkert

(1995) found that several grassland bird species exhibited major population declines in the Midwest between 1966 and 1993. DeSante and George (1994) and

Yong and Finch (1997b) reported that numerous bird

species in the Western United States were extirpated

from various States, had experienced contraction of

breeding ranges over time, or showed local population

reductions.

Declines in populations of migratory songbirds species have been attributed to events associated with

both breeding and wintering areas (Sherry and Holmes

1995). The rapid rate of deforestation in tropical areas,

for example, has been implicated in population declines of many forest-dwelling landbird migrants

(Lovejoy 1983, Rappole and others 1983, Robbins and

others 1989a). Other study results point to changes in

suitability of breeding habitat (Whitcomb 1977, Hutto

1988). For example, forest-interior migrants are reported to be area-sensitive (Robbins 1980, Robbins

and others 1989b), which explains, in part at least,

why fragmentation of forested breeding habitat has

been implicated in the population declines of migratory birds (Lynch and Whigham 1984, Wilcove 1988).

Recently, more researchers have suggested that stopover habitat along migratory routes may play an

important role in the population dynamics of migratory songbirds (Finch 1991, Moore and others 1995,

Yong and others 1998, Finch and Yong 2000). Because

the persistence of migrant populations depends on the
2 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002

bird’s ability to find favorable conditions for survival

throughout the annual cycle (Morse 1980), analyses of

population ecology need to account for factors associated with en route ecology of migrants (Moore and

Simons 1992). How well migrants “offset” costs of

migration—that is, satisfy their energy demands and

meet contingencies that arise en route—may affect

the success of an individual’s migration as well as the

future status of the population itself. As stopover

habitats are degraded or lost, the cost of migration

increases and the probability of a successful migration

decreases.

Anthropogenic modification of riparian stopover

habitats may have a greater impact on population

dynamics of songbird migrants in Western North

America where riparian and deciduous woodlands are

limited in availability than in the East (Terborgh

1989). When migrants stopover, they may have to

adjust their foraging behavior to unfamiliar habitats,

resolve conflicting demands of predator avoidance and

food acquisition, compete with other migrants and

resident birds for limiting resources, respond to unpredictable and sometimes unfavorable weather, and

correct for orientation errors (Moore and Simons 1992).

These problems are magnified when songbirds migrate through inhospitable environments such as

deserts and arrive at stopover sites with depleted

energy stores.

No systematic research has been conducted to study

stopover ecology of migratory songbirds along the

middle Rio Grande despite its potential importance to

landbird migration (Finch and Yong 2000). The objectives of this research were to (1) determine species

composition, relative abundance, and timing of spring

and fall migration of songbirds along the middle Rio

Grande, (2) evaluate the distribution of migrants among

habitats, (3) investigate the stopover biology of migrants including stopover length, energetic condition,

and amount and rate of mass gain, and (4) investigate

the potential effect of habitat differences on the stopover ecology of songbird migrants along the middle Rio

Grande of New Mexico.

Methods _______________________

Study Site

The middle Rio Grande riparian forest, locally known

as the “bosque,” represents one of the largest cottonwood (Populus spp.) gallery forests in the Southwestern United States. This wooded reach of the Rio

Grande extends from Cochiti Dam, Sandoval, NM,

downstream 260 km to San Marcial, NM (Howe and

Knopf 1991). We established netting sites at the Rio

Grande Nature Center (RGNC) (N 35°07' and W

106°41'), Bernalillo County, and at the Bosque del

Apache National Wildlife Refuge (BNWR) (N33°48'

and W106°52'), Socorro County, New Mexico. The

floodplain at these sites is typically level and broad,

varying from 2 to 10 km. Riparian forest habitat lies

within or immediately adjacent to the levees paralleling the river. The remainder of the floodplain has

largely been converted to agriculture or residential

areas. The netting locations were classified into cottonwood/native understory (including willows, mesquite, and New Mexico olive), cottonwood/Russian

olive, cottonwood/saltcedar, saltcedar, willow, or agriculture field/edge based on a riparian/wetland vegetation community classification developed by Durkin

and others (1996). The first three habitat types are

dominated by Rio Grande cottonwood (Populus

fremontii var. wislizeni) in association with a variety

of understory shrubs and small trees, chiefly willows

(Salix spp), screwbean mesquite (Prosopis pubescens),

New Mexico olive (Forestiera neomexicana), false indigo (Amorpha fruticosa), seepwillow (Baccharis

glutinosa), Russian olive (Elaeagnus angustiflia), and

saltcedar (Tamarix spp.). Cottonwood trees generally

reach 10 to 15 m in height and the foliage volume

varies depending on the age of the forest stands. The

shrub layer is best developed in more open areas of

these habitats. Cottonwood/Russian olive habitat is

characterized by a nearly monotypic understory of

dense to moderately dense Russian olive; herbaceous

growth is sparse to absent. The saltcedar habitat is

essentially dense monotypic saltcedar shrub stands

with widely scattered wolfberry (Lycium andersonii)

shrubs or patches of arrowweed (Tessaria sericea). The

willow habitat is composed of dense shrubs between

2 to 4 m tall located immediately along the banks of

irrigation water channels. The agricultural habitat is

composed primarily of alfalfa (Medicago stiva) and

corn (Zea mays) with heights not exceeding 3 m.

Capture and Recapture

We collected data in spring from 4 April to 15 June

1994, 3 April to 9 June 1995, and from 8 April to 7 June

1996, and in fall from 1 August to 13 November 1994,

from 31 July to 12 November 1995, and from 3 August

to 10 November 1996. Forty standard mist-nets (12 x

2.6 m) were used to capture birds (20 nets/site), except

in 1996 when 20 additional nests were added in BNWR,

14 of which were located at willow stands along an

irrigation channel and other six in a saltcedar stands.

The netting area for the first 20 nests at each site was

about 30 ha (net density approximately 1.5 nets/ha).

The nets were placed approximately in proportion to

availability of habitats and opportunistically within

each habitat, such as in brushy portions of wooded

areas, forest edges or breaks, or near water, where

birds could be captured most efficiently (Ralph and
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002 3

others 1993). The nets were maintained at the same,

or approximately the same, locations from year to year.

To avoid sampling bias, nets were operated simultaneously throughout the seasons and across habitat

types, and were checked regularly at 20 to 30 minute

intervals. Weather permitting, nets were opened 15

minutes before sunrise and remained open for about 6

hours every morning. For each bird captured, we

measured body mass (± 0.1 g using an electronic balance), unflattened wing chord (± 0.5 mm), tail length

(± 0.5 mm), and tarsus length (± 0.1 mm). Each bird

was banded with a numbered aluminum leg band.

Recaptured birds were reweighed without reference to

previous records. Age and sex identification was based

on Pyle and others (1987) and the North American

Bird Banding Manual (Canadian Wildlife Service and

U.S. Fish and Wildlife Service 1991).

We estimated the amount of fat stores by observing

the visible subcutaneous fat in the interclavicular

fossa and on the abdomen following a 6-point scale

(Helms and Drury 1960). Fat scores provide a fairly

precise index of fat stores as long as the variability

between observers is controlled (Krementz and

Pendleton 1990). Field assistants were trained so that

observational variation among individuals was minimized. Because the proportion of birds identified as fat

class ≥ 4 was small in this study, we combined birds

that had fat class ≥ 3 into a single group for fat class

analysis.

We estimated length of stay (stopover length) of

recaptured birds by subtracting the date of first capture from the date of last capture. This method yielded

a conservative estimate because we assumed that

birds arrived on the day of initial banding and departed on the day of last recapture (Cherry 1982).

Individuals that were not recaptured were assumed to

have left the study sites the same day they were

banded and were assigned a length of stay of zero. We

referred to the birds captured after the day of initial

capture as recaptures and all other birds as

nonrecaptures. We analyzed recapture data for all

species that had five or more individuals that were

recaptured. We were aware that this method overestimated the stopover length of species and individuals

that bred locally and interpreted results with caution.

Body mass and fat class changes during stopover

were estimated based on recaptured birds by using the

difference between the mass and fat class at initial

capture and that at last capture, respectively. Because

the majority of birds encountered in the study contributed a single data point of body mass (no recapture

data), we performed a linear regression analysis between the time and body mass at initial capture based

on all individuals captured for each species (Winker

and others 1992a). Recapture data were not used in

this analysis. We used the slope of the regression

equation as the hourly rate of body mass change. We

estimated daily percentage mass based on two methods:

1. body mass change during stopover of recaptured

birds:

(% body mass change = mass change * initial body

mass

–1 * stopover length in days–1) * 100, where mass

change is estimated from body mass difference between last capture and initial capture, and stopover

length is estimated from the difference between last

and initial capture date; and

2. regression slope of hourly rate of mass change of

all birds at initial capture:

% body mass change = (hourly rate of mass change * 8

hr * lean body mass–1) x 100,

where the lean body mass was estimated from average

body mass of all birds in the fat class 0 of each species,

hourly rate of mass change is estimated from regression slope; we assumed birds had 8 hour to gain body

mass per day.

We examined differences in the rate of mass gain

between groups by testing the equality of slopes between regression models with analysis of covariance

(ANCOVA). We assumed that water gain or loss was

negligible (Nisbet and others 1963, Rogers and Odum

1994, 1966, Winker 1995).

We followed Robbins and others (1989b) and Hussell

and others (1992) in classifying species as tropical or

temperate migrants in accordance with winter distributions (AOU 1998, Root 1988). Migratory species

wintering mainly north of 25°N were classified as

temperate and all others were tropical migrants.

Data Analysis

The netting efforts were recorded as net hr (nethour), that is, one net operated for 1 hr = 1 net hr. To

compare the relative abundance of migrants among

habitats, we adjusted net hr variation by calculating a

capture-rate index for each species in relation to each

habitat. We divided the total number of captures by

the total number of net hr in a given habitat, and then

multiplied the result by 1,000. This calculation produced an abundance index of birds/1,000 net hr. We

used total number of species captured, Shannon diversity, and evenness indices (Krebs 1998) to estimate

avian species richness and diversity in each habitat.

Shannon diversity and evenness indices were based

on birds/1,000 net hr so that netting effort variations

among habitat types were adjusted. To compare the

species composition between habitats, we used

Jaccard’s similarity coefficient: Sj = a * (a + b + c)–1,

where a is the number of species occurring in both

habitats, and b and c are the number of species occurring only in the first or the second habitat, respectively (Krebs 1998). To summarize bird community
4 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002

characteristics, we applied a detrended correspondence analysis (DCA). DCA is an ordination method

for adapting a multidimensional dataset (species and

habitat) in such a way that when it is projected onto a

reduced dimensional space, the intrinsic pattern that

the dataset may possess becomes apparent (Pielou

1984). We used DCA instead of principle component

analysis because we expected that migrants might not

respond linearly to environment gradients. To control

variation in sampling effort among habitats and to

avoid the disproportional effect of accidental or rare

species on the analyses, DCA was based on relative

abundance data (birds/1,000 net hr) and on species

with captures ≥ 1 bird/1,000 net hr.

Our research represents the first comprehensive

study of stopover ecology of migratory landbirds in

both spring and fall along the middle Rio Grande.

Hink and Ohmart (1984) studied riparian habitats

and associated terrestrial vertebrate communities of

the middle Rio Grande from 1981 to 1983. Their study

provided a comprehensive dataset on landbird species

composition and relative abundance in the area. As a

follow-up to Hink and Ohmart, and to assess bird

population changes, Hoffman (1990) conducted avian

surveys in the middle Rio Grande Valley State Park

from 1987 to 1990. We compared our data with results

from these two studies by applying a relative abundance scale modified from Hink and Ohmart (1984)

and Hoffman (1990). We classified 146 captured species into four categories based on relative capture rate:

Abundant (≥ 5 birds/1,000 net hr), Common (≥ 1 birds/

1,000 net, but < 5 birds/1,000 net hr), Uncommon (≥ 0.1

birds/1,000 net hr, but < 1 birds/1,000 net hr), R = rare

(< 0.1 birds/1,000 net hr) (table 1). We then examined

the relationship among these datasets with Spearman’s

rank correlation.

Table 1—Comparison of species composition and relative abundance of middle Rio Grande migrants captured during this

study and recorded by Hoffman (1990) and Hink and Ohmart (1984). Species are arranged in the order of total

captures. See appendix I for species codes and taxonomic groups.

Species Total1 %2 Birds/1,000nh3 Class4 Hoffman5 Hink & Ohmart6

WIWA 3354 14.09 35.02 A C A

CHSP 2706 11.37 28.25 A R C

PISI 1687 7.09 17.61 A C C

WCSP 1457 6.12 15.21 A A A

MGWA 1304 5.48 13.61 A C C

YRWA 870 3.66 9.08 A C A

DEJU 857 3.60 8.95 A A A

OCWA 831 3.49 8.68 A R C

RCKI 636 2.67 6.64 A U C

LAZB 623 2.62 6.50 A R C

LASP 567 2.38 5.92 A C

BRSP 506 2.13 5.28 A C

YWAR 489 2.05 5.11 A R C

SOSP 446 1.87 4.66 C A C

BLGR 430 1.81 4.49 C C A

LISP 416 1.75 4.34 C R U

DUFL 398 1.67 4.16 C C

LEGO 397 1.67 4.15 C C C

BHGR 349 1.47 3.64 C A A

HOFI 324 1.36 3.38 C C C

AMGO 295 1.24 3.08 C U C

HETH 254 1.07 2.65 C U C

SPTO 251 1.05 2.62 C U C

VIWA 248 1.04 2.59 C C

VESP 248 1.04 2.59 C U

RWBL 238 1.00 2.48 C C A

WAVI 229 0.96 2.39 C C C

GTTO 225 0.95 2.35 C R C

AMRO 221 0.93 2.31 C A C

SAVS 217 0.91 2.27 C C

WEWP 215 0.90 2.24 C U C

WIFL 213 0.89 2.22 C R C

COYE 213 0.89 2.22 C U C

(con.)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002 5

(con.)

CCSP 202 0.85 2.11 C R

BEWR 160 0.67 1.67 C U C

HOWR 153 0.64 1.60 C U C

WETA 139 0.58 1.45 C C C

SUTA 107 0.45 1.12 C R C

BUOR 82 0.34 0.86 U U U

WEFL 72 0.30 0.75 U R

GRFL 70 0.29 0.73 U U

SOVI 64 0.27 0.67 U R U

BLPH 56 0.24 0.58 U R C

YBCH 55 0.23 0.57 U U C

RSFL 46 0.19 0.48 U C C

HAFL 41 0.17 0.43 U R

MOCH 41 0.17 0.43 U U R

ATFL 36 0.15 0.38 U U C

WEKI 35 0.15 0.37 U R C

BHCO 34 0.14 0.35 U A C

BCCH 33 0.14 0.34 U C U

NOWA 33 0.14 0.34 U U

COBU 32 0.13 0.33 U U

MODO 30 0.13 0.31 U A A

WBNU 30 0.13 0.31 U U U

BRCR 28 0.12 0.29 U U C

BARS 24 0.10 0.25 U C C

DOWO 21 0.09 0.22 U U C

NRWS 21 0.09 0.22 U U C

INBU 21 0.09 0.22 U R C

SCJA 18 0.08 0.19 U U C

GRCA 18 0.08 0.19 U R C

NOMO 18 0.08 0.19 U R C

TOWA 15 0.06 0.16 U R

WTSP 15 0.06 0.16 U R U

OVEN 14 0.06 0.15 U R

YHBL 14 0.06 0.15 U R C

GTGR 14 0.06 0.15 U C U

SSHA 13 0.05 0.14 U U U

YBCU 13 0.05 0.14 U R U

RBNU 13 0.05 0.14 U R R

LARB 13 0.05 0.14 U R

SAPH 12 0.05 0.13 U R C

BGGN 12 0.05 0.13 U U U

SWTH 11 0.05 0.11 U R R

OSFL 10 0.04 0.10 R R C

MAWR 10 0.04 0.10 R R C

RNSA 9 0.04 0.09 R U

NAWA 9 0.04 0.09 R R

COHA 8 0.03 0.08 R U C

HAWO 7 0.03 0.07 R R R

BEKI 6 0.03 0.06 R U C

LUWA 6 0.03 0.06 R R

PABU 6 0.03 0.06 R

WEME 6 0.03 0.06 R U C

CAFI 6 0.03 0.06 R R R

AMKE 5 0.02 0.05 R U C

BCFL 5 0.02 0.05 R

GCKI 5 0.02 0.05 R R

LBWO 4 0.02 0.04 R R

LEFL 4 0.02 0.04 R R

VERD 4 0.02 0.04 R U

BTYW 4 0.02 0.04 R R U

Table 1 (Con.).

Species Total1 %2 Birds/1,000nh3 Class4 Hoffman5 Hink & Ohmart6
6 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002

BAWW 4 0.02 0.04 R R

HETA 4 0.02 0.04 R

RBGR 4 0.02 0.04 R R

CANT 4 0.02 0.04 R U

TOSO 3 0.01 0.03 R R R

PROW 3 0.01 0.03 R R

MAWA 3 0.01 0.03 R R

AMRE 3 0.01 0.03 R R

KEWA 3 0.01 0.03 R R

HOWA 3 0.01 0.03 R R

BTSP 3 0.01 0.03 R C

GRSP 3 0.01 0.03 R

SWSP 3 0.01 0.03 R R R

OROR 3 0.01 0.03 R

GRRO 2 0.01 0.02 R C C

WISA 2 0.01 0.02 R R

ROWR 2 0.01 0.02 R R

GRVI 2 0.01 0.02 R

REVI 2 0.01 0.02 R R R

CSWA 2 0.01 0.02 R

PYRR 2 0.01 0.02 R R

DICK 2 0.01 0.02 R R

WWDO 1 0.00 0.01 R

CONI 1 0.00 0.01 R R C

VGSW 1 0.00 0.01 R C C

BANS 1 0.00 0.01 R R U

PLTI 1 0.00 0.01 R U

CACW 1 0.00 0.01 R

WIWR 1 0.00 0.01 R R

CARW 1 0.00 0.01 R

SATH 1 0.00 0.01 R R

BRTH 1 0.00 0.01 R R

CRTH 1 0.00 0.01 R U

BEVI 1 0.00 0.01 R

YTVI 1 0.00 0.01 R R

BWWA 1 0.00 0.01 R

LAWA 1 0.00 0.01 R

BTBW 1 0.00 0.01 R

BLBW 1 0.00 0.01 R

PRAW 1 0.00 0.01 R

WPWA 1 0.00 0.01 R R

TEWA 1 0.00 0.01 R R R

MOWA 1 0.00 0.01 R

CAWA 1 0.00 0.01 R

SCTA 1 0.00 0.01 R

CASP 1 0.00 0.01 R

ATSP 1 0.00 0.01 R U

SAGS 1 0.00 0.01 R U

FOSP 1 0.00 0.01 R R

GCSP 1 0.00 0.01 R R

COGR 1 0.00 0.01 R U

WWCR 1 0.00 0.01 R

EVGR 1 0.00 0.01 R U R

1

Total birds captured (not include recaptures).

2 Percent of all captures (23,800 birds).

3

Birds captured/1,000 net-hr.

4

Relative abundance class based on capture rate: A = abundant (capture rate ≥ 5 birds/1,000 net hr), C = common (1 birds/1,000 net

hr ≤ capture rate < 5 birds/1,000 net hr), U = uncommon (0.1 birds/1,000 net hr ≤ capture rate < 1 birds/1,000 net hr), R = Rare (capture

rate < 0.1 birds/1,000 net hr). 5

“Accidental” species group in the original study (Hoffman 1990) was combined with “rare” species group for the comparison.

6

“Fairly common” species group in the original study (Hink and Ohmart 1984) was combined with “common” species group for the

comparison.

Table 1 (Con.).

Species Total1 %2 Birds/1,000nh3 Class4 Hoffman5 Hink & Ohmart6
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002 7

Normality and homogeneity testing variables were

examined. We applied a fixed effect general linear

model to examine among-group variations when assumptions for parametric statistics were met, and

used nonparametrical procedures otherwise. After

careful examination of among-year variation, we combined the 3 years of data for all the analyses. We also

grouped the data from the two sites for some analyses.

Data grouping was justified based on the following

reasons: (1) we were interested in the general stopover

patterns of migratory landbirds along the middle Rio

Grande, (2) our research was exploratory, (3) data

combination made our analysis more robust to amongyear or between-site variations, (4) the two sites were

close to each other (approximately 100 km), (5) net

locations at the two study sites represented different

habitats in most cases, and (6) combining data allowed

us to test some hypotheses that otherwise would not be

possible because of sample size limitations. Data grouping should have limited effect about our conclusions

because we operated mist-nets concurrently at most

net locations during the study, and variation among

years was small.

Means and standard deviations are reported through

the manuscript. A statistical significance level of a =

0.05 was used unless otherwise indicated. Most analyses were performed with SPSS/PC (SPSS 1996). Shannon diversity and evenness indices, Jaccard’s similarity coefficient were calculated with MVSP software

(Kovach 1995), and detrended correspondence analysis was performed with CANOCO (ter Braak and

Smilauer 1998).

Results ________________________

Species Composition and Relative

Abundance

We operated 35,190 net hr in spring and 60,587 net

hr in fall over 3 years. Between-season variation in

netting effort was due to a longer migration season in

fall. On average, we operated 43 ± 3 days/year in

spring and 63 ± 4 days/year in fall at BNWR, and 47 ±

3 days/year in spring and 94 ± 5 days/year in fall at

RGNC. In total, 23,800 landbirds, representing 146

species, were banded during the study. Neotropical

migrants accounted for 77 species and 62.6 percent of

all captures, temperate migrants accounted for 47

species and 35.7 percent of all captures, and resident

species accounted for 18 species and 1.6 percent of the

total captures. The White-winged Dove, Brown-crested

Flycatcher, Cactus Wren, and Lucy’s Warbler bred

primarily south of the U.S. and Mexican border and

entered the United States along the Rio Grande Valley; these four species accounted for 0.1 percent of the

total captures. The five most abundant species made

up 44 percent of total captures. Wilson’s Warbler was

the most abundant species (12.1 percent), followed by

the Chipping Sparrow (11.2 percent), Pine Siskin (7.1

percent), White-crowned Sparrow (6.1 percent), and

MacGillivray’s Warbler (5.5 percent) (table 2). The

next five most abundant species were Yellow-rumped

Warbler (3.7 percent), Dark-eyed Junco (3.6 percent),

Orange-crowned Warbler (3.5 percent), Ruby-crowned

Kinglet (2.7 percent), and Lazuli Bunting (2.6 percent)

(table 2).

Species richness and relative abundance were lower

in spring than in fall. Of 23,800 birds, 4,673 (19.6

percent) birds of 108 species were captured in spring

and 19,127 (80.4 percent) birds of 125 species were

captured in fall. Capture rate on average was 133

birds/1,000 net hr in spring and 315 birds/1,000 net hr

in fall. In spring, 10 species made up 65 percent of the

captures; the MacGillivray’s Warbler was the most

abundant species (16.9 percent), followed by the

Wilson’s Warbler (14.1 percent), White-crowned Sparrow (6.1 percent), Dusky Flycatcher (5.6 percent),

Yellow-rumped Warbler (5.5 percent), Red-winged

Blackbird (4.2 percent), Black-headed Grosbeak (3.9

percent), Common Yellowthroat (2.8 percent), Yellow

Warbler (2.7 percent), and American Robin (2.7 percent) (table 2). In fall, 10 species made up 63 percent

of the captures; the Wilson’s Warbler was the most

abundant species (14.1 percent), followed by the Chipping Sparrow (13.9 percent), Pine Siskin (8.8 percent),

White-crowned Sparrow (6.1 percent), Dark-eyed Junco

(4.1 percent), Orange-crowned Warbler (4.0 percent),

Yellow-rumped Warbler (3.2 percent), Lazuli Bunting

(3.2 percent), Ruby-crowned Kinglet (2.9 percent), and

Lark Sparrow (2.9 percent) (table 2).

Twenty-one species were captured only during spring

migration (spring-only species). Most were Parulinae

warblers including species such as the Blackburnian

Warbler, Blue-winged Warbler, Canada Warbler,

Chestnut-sided Warbler, Hooded Warbler, Kentucky

Warbler, Lawrence’s Warbler, Magnolia Warbler,

Mourning Warbler, and Prairie Warbler. These species breed mostly in the Eastern North America and

are considered as rare and accidental species along the

middle Rio Grande during spring migration (Hubbard

1978). Of the 21 spring-only species, 76.2 percent were

Neotropical migrants, 14.3 percent were temperate

migrants, and 9.5 percent had a breeding range along

the U.S./Mexico board. Thirty-eight species were captured only during fall migration (fall-only species).

Compared to spring-only species, fall-only species were

composed of more diverse taxonomic groups, more

temperate migratory species (45 percent) and fewer

Neotropical migratory species (36.8 percent), and fewer

Eastern breeding species (the Least Flycatcher, Orchard Oriole, Painted Bunting, Prothonotary Warbler, Tennessee Warbler, and Scarlet Tanager).
8 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002

Table 2—Landbirds captured during spring and fall migration of 1994, 1995, and 1996 at Bosque del Apache National

Wildlife Refuge (BNWR) and Rio Grande Nature Center (RGNC) of the middle Rio Grande Valley of New

Mexico.

BNWR RGNC Total by season and site

Species Spring Fall Spring Fall Spring Fall BNWR RGNC Total

SSHA 1 0 0 12 1 12 1 12 13

COHA 0 1 2526 1 7 8

AMKE 1 0 1 3 2 3 1 4 5

MODO 0 0 13 17 13 17 0 30 30

WWDO 0 0 1010 0 1 1

YBCU 7 6 0 0 7 6 13 0 13

GRRO 0 1 0102 1 1 2

CONI 0 0 0101 0 1 1

BEKI 0 0 0606 0 6 6

DOWO 1 6 5 9 6 15 7 14 21

HAWO 2 5 0025 7 0 7

RNSA 0 5 0409 5 4 9

WISA 0 1 0 1 0 2 1 1 2

LBWO 2 2 0022 4 0 4

RSFL 2 11 1 32 3 43 13 33 46

OSFL 6 0 1 3 7 3 6 4 10

WEWP 56 37 43 79 99 116 93 122 215

HAFL 9 9 1 22 10 31 18 23 41

LEFL 0 0 0404 0 4 4

WIFL 47 34 46 86 93 120 81 132 213

DUFL 177 33 85 103 262 136 210 188 398

GRFL 19 21 13 17 32 38 40 30 70

WEFL 6 21 3 42 9 63 27 45 72

BLPH 22 2 10 22 32 24 24 32 56

SAPH 1 1 0 10 1 11 2 10 12

ATFL 23 0 10 3 33 3 23 13 36

BCFL 3 2 0032 5 0 5

WEKI 2 0 3 30 5 30 2 33 35

BARS 7 6 4 7 11 13 13 11 24

VGSW 1 0 0010 1 0 1

BANS 0 0 1010 0 1 1

NRWS 19 0 1 1 20 1 19 2 21

SCJA 0 5 5 8 5 13 5 13 18

MOCH 1 6 0 34 1 40 7 34 41

BCCH 0 0 6 27 6 27 0 33 33

PLTI 0 0 0101 0 1 1

COBU 0 1 1 30 1 31 1 31 32

VERD 0 4 0004 4 0 4

RBNU 0 6 0 7 0 13 6 7 13

WBNU 5 7 7 11 12 18 12 18 30

BRCR 0 13 0 15 0 28 13 15 28

ROWR 0 1 0102 1 1 2

CACW 0 1 0001 1 0 1

BEWR 31 57 4 68 35 125 88 72 160

HOWR 39 21 20 73 59 94 60 93 153

MAWR 1 5 1 3 2 8 6 4 10

WIWR 0 1 0001 1 0 1

CARW 1 0 0010 1 0 1

RCKI 56 369 17 194 73 563 425 211 636

GCKI 2 1 0223 3 2 5

BGGN 4 0 5 3 9 3 4 8 12

TOSO 1 2 0012 3 0 3

SWTH 6 4 0 1 6 5 10 1 11

HETH 24 33 41 156 65 189 57 197 254

AMRO 32 2 93 94 125 96 34 187 221 (con.)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002 9

Table 2 (Con.)

BNWR RGNC Total by season and site

Species Spring Fall Spring Fall Spring Fall BNWR RGNC Total

GRCA 5 7 4 2 9 9 12 6 18

NOMO 7 0 10 1 17 1 7 11 18

SATH 0 0 1010 0 1 1

BRTH 0 1 0001 1 0 1

CRTH 0 1 0001 1 0 1

BEVI 0 0 1010 0 1 1

GRVI 1 0 1020 1 1 2

SOVI 18 16 1 29 19 45 34 30 64

WAVI 57 64 22 86 79 150 121 108 229

REVI 1 1 0011 2 0 2

YTVI 1 0 0010 1 0 1

PROW 0 2 0103 2 1 3

BWWA 1 0 0 0 1 0 1 0 1

LAWA 0 0 1 0 1 0 0 1 1

OCWA 45 94 18 674 63 768 139 692 831

NAWA 4 0 1454 4 5 9

MAWA 2 0 1030 2 1 3

VIWA 29 59 14 146 43 205 88 160 248

LUWA 6 0 0060 6 0 6

YWAR 83 131 43 232 126 363 214 275 489

CSWA 2 0 0020 2 0 2

YRWA 79 105 177 509 256 614 184 686 870

BTYW 2 0 0222 2 2 4

BTBW 0 1 0001 1 0 1

BLBW 0 0 1010 0 1 1

TOWA 3 5 0 7 3 12 8 7 15

PRAW 1 0 0010 1 0 1

BAWW 1 2 0 1 1 3 3 1 4

AMRE 1 2 0012 3 0 3

WPWA 0 0 1 0 1 0 0 1 1

NOWA 0 10 7 16 7 26 10 23 33

OVEN 9 0 4 1 13 1 9 5 14

TEWA 0 0 0101 0 1 1

MOWA 1 0 0010 1 0 1

MGWA 319 145 473 367 792 512 464 840 1304

COYE 114 65 15 19 129 84 179 34 213

KEWA 2 0 1030 2 1 3

CAWA 1 0 0010 1 0 1

WIWA 547 1026 110 1671 657 2697 1573 1781 3354

HOWA 2 0 1030 2 1 3

YBCH 38 10 3 4 41 14 48 7 55

HETA 0 2 0204 2 2 4

SUTA 34 65 4 4 38 69 99 8 107

WETA 8 44 7 80 15 124 52 87 139

SCTA 0 0 0101 0 1 1

PYRR 0 2 0002 2 0 2

RBGR 2 0 1131 2 2 4

BHGR 97 80 84 88 181 168 177 172 349

BLGR 77 121 20 212 97 333 198 232 430

LAZB 6 43 4 570 10 613 49 574 623

INBU 7 6 2 6 9 12 13 8 21

PABU 0 3 0306 3 3 6

DICK 0 0 0202 0 2 2

GTTO 21 22 51 131 72 153 43 182 225

CASP 0 0 1010 0 1 1

SPTO 61 79 7 104 68 183 140 111 251

(con.)
10 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002

CANT 1 0 0313 1 3 4

CHSP 34 392 7 2273 41 2665 426 2280 2706

ATSP 0 0 0101 0 1 1

CCSP 0 40 0 162 0 202 40 162 202

BRSP 58 111 8 329 66 440 169 337 506

VESP 4 6 2 236 6 242 10 238 248

LASP 5 63 1 498 6 561 68 499 567

BTSP 1 2 0012 3 0 3

SAGS 0 1 0001 1 0 1

LARB 0 0 0 13 0 13 0 13 13

SAVS 0 56 23 138 23 194 56 161 217

GRSP 0 0 0303 0 3 3

SOSP 7 244 7 188 14 432 251 195 446

FOSP 0 1 0001 1 0 1

LISP 15 160 11 230 26 390 175 241 416

SWSP 0 1 0203 1 2 3

WTSP 2 5 2 6 4 11 7 8 15

GCSP 0 0 0101 0 1 1

WCSP 178 373 106 800 284 1173 551 906 1457

DEJU 56 327 14 460 70 787 383 474 857

YHBL 0 1 0 13 0 14 1 13 14

RWBL 1 1 197 39 198 40 2 236 238

WEME 0 0 1515 0 6 6

COGR 0 0 0101 0 1 1

GTGR 0 0 12 2 12 2 0 14 14

BHCO 21 2 6 5 27 7 23 11 34

OROR 0 0 0303 0 3 3

BUOR 11 10 3 58 14 68 21 61 82

CAFI 0 0 0606 0 6 6

HOFI 18 2 1 303 19 305 20 304 324

PISI 0 0 0 1687 0 1687 0 1687 1687

LEGO 9 16 1 371 10 387 25 372 397

WWCR 0 0 0101 0 1 1

AMGO 2 4 0 289 2 293 6 289 295

EVGR 0 0 0101 0 1 1

Total 2735 4775 1938 14352 4673 19127 7510 16291 23800

Table 2 (Con.)

BNWR RGNC Total by season and site

Species Spring Fall Spring Fall Spring Fall BNWR RGNC Total

More species and more individuals were captured at

BNWR (2,735 individuals of 91 species) than at RGNC

(1,938 individuals of 81 species) in spring. The pattern

was reversed in fall: 4,775 individuals of 91 species at

BNWR and 14,352 individuals of 108 species at RGNC.

The species composition and relative abundance of

each species were generally similar between the two

sites. The relative abundance of species was correlated

between the two sites in spring (rs = 0.60, P < 0.001)

and in fall (rs = 0.65, P < 0.001) (table 2). There is a high

similarity of species composition between the two

sites: 90 species were captured at both sites while 26

species were captured at BNWR only (BNWR-only

species), and 29 species were captured at RGNC only

(RGNC-only species). Of these 55 single-site species,

31 species (56 percent) were captured only once during

the study; most of these species were rare or accidental

in the area. There was a tendency for edge or suburban-related species (for example, the American Robin,

House Finch, American Goldfinch, Pine Siskin, and

Lesser Goldfinch) to be more abundant at RGNC.

Whereas desert species or species breeding along the

border of Mexico and the United States (such as the

Ladder-backed Woodpecker, Pyrrhuloxia, Verdin,

Brown-crested Flycatcher, Lucy’s Warbler, and Cactus Wren) were more likely to be captured at BNWR.

In contrast to the 146 species we captured, Hink and

Ohmart detected 124 species while Hoffman recorded

79 species. Twenty-one species captured during our

study were not recorded in either study; most of these
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002 11

species were rare or accidental. Among them, 12 species (Blackburnian Warbler, Black-throated Blue

Warbler, Blue-winged Warbler, Carolina Wren,

Canada Warbler, Chestnut-sided Warbler, Lawrence’s

Warbler, Mourning Warbler, Orchard Oriole, Painted

Bunting, Prairie Warbler, and Scarlet Tanager) were

Eastern breeding species while six (Brown-crested

Flycatcher, Belted Vireo, Cactus Wren, Cassin’s Sparrow, Gray Vireo, and White-winged Dove) had breeding ranges restricted mostly to Mexico and Southwestern United States. Our classification of 146 captured

species was correlated with Hink and Ohmart’s (1984)

classification (Spearman’s r = 0.64, P < 0.001, n = 124)

and Hoffman’s (1990) (Spearman’s r = 0.35, P < 0.005,

n = 79) (table 1). Hink and Ohmart’s (1984) and

Hoffman’s (1990) species classifications were also correlated (Spearman’s r = 0.50, P < 0.001, n = 78).

Migration Timing

During our daily mist-netting operation, capture

rate was the highest during the morning hours and

gradually declined after 0900 am (MST) both in spring

and fall (fig. 1). We found that proportionally more

Neotropical migratory birds were captured in the

early hours than temperate migratory species in spring

(X2 = 25.54, df = 7, P < 0.005) and in fall (X2 = 407.71,

df = 7, P < 0.005, fig.1). Daily mean capture time of

Neotropical migrants was about 40 minutes earlier in

spring and was about 30 minutes earlier in fall than

that of resident species.

Seasonally, spring migration peaked in late-April

and the first half of May, while fall migration peaked

after the last week of August and continued through

mid-October (fig. 2). In general, temperate migrants

migrated through our sites earlier than Neotropical

migrants in spring, and the pattern was reversed in

fall (fig. 2). The mean capture date for temperate

migrants was Julian day 121 and 284 in spring and

fall, respectively, while it was Julian day 137 and 252

for Neotropical migrants in spring and fall, respectively. The mean capture date differed by 16 days in

spring and by 32 days in fall between the two groups.

We tested seasonal passage time between the two

sites in spring and fall for 27 species that had ≥ 5

captures/site/season (table 3). In spring, the majority

of these species (67 percent) had a similar passage

time between the two sites. In spring, the median

passage date at BNWR was earlier than RGNC for six

species: the Western Wood-Pewee, Willow Flycatcher,

Warbling Vireo, Black-headed Grosbeak, Green-tailed

Towhee, and Brewer’s Sparrow; and was earlier at

RGNC than BNWR for three species: the Yellowrumped Warbler, Hermit Thrush, and White-crowned

Sparrow. In fall, the median passage time did not

differ between the two sites for 13 of the 27 species; but

Figure 1—Temporal distribution of daily captures of

migrants during spring and fall migration stopover

along the middle Rio Grande.

it was earlier at BNWR than RGNC for eight species:

the Gray Flycatcher, Yellow Warbler, Common Yellowthroat, Black-headed Grosbeak, Green-tailed Towhee, Spotted Towhee, Chipping Sparrow, and Song

Sparrow. It was earlier at RGNC for six species: the

White-breasted Nuthatch, Ruby-crowned Kinglet,

Orange-crowned Warbler, Yellow-rumped Warbler,

Lincoln’s Sparrow, and White-crowned Sparrow (table

3). Of these 27 species, the median capture dates in

spring were negatively correlated with that in fall [r =

- 0.73, n = 27, P < 0.001 for BNWR (fig. 3a); r = - 0.70,

n = 27, P < 0.01 for RGNC (fig. 3b)]. This pattern

suggests that temporal migration patterns were reversed for a given species between spring and fall

migration, that is, species that migrated through the

30

20

Spring

25

15

10

5

0

Percent of total capture

600 700 800 900

Time (MST)

Neotropical (n = 3,451)

Temperate (n = 1,125)

1000 1100 1200 1300

20

Fall 25

15

10

5

0

Percent of total capture

600 700 800 900

Time (MST)

Neotropical (n = 11,454)

Temperate (n = 7,365)

1000 1100 1200 1300
12 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002

Figure 2—Distribution of weekly captures of landbirds during spring and fall migration along the

middle Rio Grande. Week 14 is the first week in April.

Table 3—Median (range) capture date (Julian day) of landbirds during spring and fall migration at Bosque del Apache National

Wildlife Refuge (BNWR) and Rio Grande Nature Center (RGNC). See table 2 for sample sizes and appendix I for

species codes.

Spring Fall

Species BNWR RGNC Combined BNWR RGNC Combined

SSHA 118 (0) 118 (0) 291 (51) 291 (51)

COHA 120 (19) 120 (19) 222 (0) 248 (48) 241 (48)

AMKE 111 (0) 150 (0) 131 (39) 244 (70) 244 (70)

MODO 138 (41) 137 (41) 264 (97) 264 (97)

WWDO 115 (0) 115 (0)

YBCU 157 (10) 156 (10) 223 (20) 223 (20)

GRRO 243 (0) 240 (0) 242 (3)

CONI 262 (0) 262 (0)

BEKI 241 (40) 241 (40)

DOWO 160 (0) 158 (45) 152 (45) 244 (70) 266 (81) 253 (89)

HAWO 103 (9) 103 (9) 271 (74) 271 (74)

RNSA 299 (46) 280 (23) 285 (46)

WISA 274 (0) 290 (0) 282 (16)

LBWO 151 (14) 151 (14) 233 (21) 233 (21)

RSFL 139 (41) 107 (0) 128 (52) 285 (57) 278 (49) 285 (69)

OSFL 129 (9) 140 (0) 130 (17) 251 (19) 251 (19)

WEWP 138 (34) 149 (20)*** 143 (39) 253 (42) 247 (49)ns 250 (49)

HAFL 141 (17) 144 (0) 141 (17) 256 (48) 266 (38) 266 (52)

LEFL 250 (20) 250 (20)

WIFL 150 (25) 152 (27)* 149 (27) 245 (53) 240 (39)ns 240 (54)

DUFL 131 (40) 130 (37)ns 133 (40) 249 (63) 250 (57)ns 249 (64)

GRFL 130 (47) 123 (39)ns 128 (48) 234 (87) 244 (46)* 242 (87)

(con.)

50

40

60

30

20

10

0

Capture rate (birds/100 net-hr)

14 15 16 17

Week

Neotropical

Temperate

18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002 13

WEFL 143 (11) 150 (8) 145 (13) 248 (41) 244 (59) 245 (59)

BLPH 139 (63) 114 (62) 131 (63) 249 (4) 255 (100) 253 (100)

SAPH 114 (0) 114 (0) 254 (0) 262 (93) 260 (93)

ATFL 130 (44) 132 (41) 132 (47) 226 (16) 226 (16)

BCFL 123 (8) 123 (8) 250 (9) 250 (9)

WEKI 142 (30) 138 (4) 139 (30) 230 (47) 230 (47)

BARS 131 (34) 138 (46) 137 (46) 254 (24) 226 (26) 241 (31)

VGSW 146 (0) 146 (0)

BANS 128 (0) 128 (0)

NRWS 126 (41) 153 (0) 128 (44) (82) 240 (0) 240 (0)

SCJA 124 (49) 125 (49) 273 (33) 264 (40) 265 (40)

MOCH 129 (0) 129 (0) 299 (13) 283 (83) 283 (83)

PLTI 270 (0) 270 (0)

BCCH 157 (43) 157 (43) 245 (99) 245 (99)

COBU 101 (0) 101 (0) 311 (0) 243 (99) 260 (100)

VERD 290 (35) 290 (35)

RBNU 253 (60) 272 (41) 263 (60)

WBNU 130 (54) 152 (52)ns 139 (57) 271 (42) 247 (66)** 260 (73)

BRCR 299 (28) 286 (33) 294 (35)

ROWR 256 (0) 291 (0) 274 (35)

CACW 278 (0) 278 (0)

BEWR 121 (56) 117 (15) 123 (56) 262 (101) 263 (99) 259 (102)

HOWR 134 (32) 136 (42)ns 135 (42) 256 (43) 257 (51)ns 259 (51)

MAWR 151 (0) 124 (0) 138 (27) 298 (41) 267 (21) 284 (45)

WIWR 306 (0) 306 (0)

CARW 130 (0) 130 (0)

RCKI 120 (43) 117 (23)ns 119 (43) 292 (60) 279 (62)*** 288 (62)

GCKI 125 (57) 125 (57) 290 (0) 288 (25) 288 (25)

BGGN 115 (7) 121 (6) 118 (14) 262 (13) 261 (13)

TOSO 124 (0) 124 (0) 280 (2) 280 (2)

SWTH 139 (13) 141 (13) 261 (16) 270 (0) 263 (17)

HETH 133 (51) 123 (56) 126 (56) 291 (44) 290 (62)ns 289 (62)

AMRO 109 (57) 114 (71) 121 (71) 279 (23) 283 (99) 275 (99)

GRCA 146 (28) 142 (14) 145 (28) 256 (65) 286 (16) 264 (65)

NOMO 143 (40) 128 (21) 132 (40) 235 (0) 235 (0)

SATH 105 (0) 105 (0)

BRTH 289 (0) 289 (0)

CRTH 233 (0) 233 (0)

BEVI 109 (0) 109 (0)

GRVI 128 (0) 128 (0) 128 (0)

SOVI 128 (34) 118 (0) 130 (34) 256 (50) 264 (41) 261 (50)

WAVI 146 (29) 154 (17)** 148 (29) 229 (50) 236 (61)ns 236 (61)

REVI 149 (0) 149 (0) 263 (0) 263 (0)

YTVI 128 (0) 128 (0)

PROW 259 (19) 273 (0) 263 (24)

BWWA 143 (0) 143 (0)

LAWA 136 (0) 136 (0)

OCWA 131 (46) 127 (38)ns 131 (46) 271 (77) 265 (68)*** 265 (77)

NAWA 124 (12) 118 (0) 125 (17) 262 (24) 262 (24)

VIWA 131 (36) 131 (27)ns 130 (36) 236 (68) 241 (60)ns 243 (72)

LUWA 117 (27) 116 (27)

MAWA 148 (5) 140 (0) 145 (10)

YWAR 132 (34) 136 (46)ns 135 (46) 234 (51) 239 (53)** 239 (53)

CSWA 154 (4) 154 (4)

YRWA 123 (38) 119 (52)** 121 (52) 278 (60) 271 (63)*** 274 (63)

BTYW 136 (10) 136 (10) 228 (14) 228 (14)

(con.)

Table 3 (Con.)

Spring Fall

Species BNWR RGNC Combined BNWR RGNC Combined
14 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002

BTBW 275 (0) 275 (0)

BLBW 156 (0) 156 (0)

TOWA 145 (2) 144 (2) 262 (27) 263 (35) 262 (38)

PRAW 149 (0) 149 (0)

BAWW 137 (0) 137 (0) 250 (7) 249 (0) 250 (7)

AMRE 142 (0) 142 (0) 273 (17) 273 (17)

WPWA 140 (0) 140 (0)

NOWA 131 (15) 130 (15) 242 243 (35) 243 (86)

OVEN 137 (21) 147 (23) 141 (23) 252 (0) 252 (0)

TEWA 253 (0) 253 (0)

MOWA 132 (0) 132 (0)

MGWA 143 (42) 143 (38)ns 142 (44) 250 (67) 250 (66)ns 250 (68)

COYE 137 (46) 135 (33)ns 138 (46) 249 (100) 261 (61)* 250 (100)

KEWA 153 (11) 141 (0) 149 (17)

CAWA 143 (0) 143 (0)

WIWA 138 (51) 142 (51)ns 136 (55) 254 (69) 253 (75)ns 254 (75)

HOWA 151 (14) 141 (0) 148 (17)

YBCH 142 (31) 152 (35) 142 (35) 237 (66) 252 (41)ns 245 (66)

HETA 250 (20) 235 (0) 243 (25)

SUTA 136 (34) 145 (8) 140 (34) 243 (56) 222 (15) 239 (56)

WETA 142 (26) 133 (23)ns 137 (26) 250 (58) 249 (70)ns 247 (70)

SCTA 221 (0) 221 (0)

PYRR 305 (12) 305 (12)

RBGR 130 (1) 128 (0) 129 (2) 274 (0) 274 (0)

BHGR 143 (39) 149 (48)* 144 (48) 225 (60) 233 (52)*** 231 (60)

BLGR 145 (32) 145 (26)ns 145 (34) 245 (62) 249 (65)ns 247 (69)

LAZB 139 (19) 123 (34) 134 (38) 249 (44) 239 (54) 240 (54)

INBU 155 (17) 154 (2) 152 (17) 238 (39) 239 (30) 237 (39)

PABU 255 (24) 257 (42) 254 (60)

DICK 250 (12) 250 (12)

GTTO 128 (20) 132 (42)** 132 (43) 250 (34) 259 (56)** 258 (56)

SPTO 131 (66) 134 (63)ns 132 (71) 269 (99) 283 (90)*** 274 (104)

CANT 127 (0) 127 (0) 274 (3) 274 (3)

CASP 125 (0) 125 (0)

CHSP 122 (44) 123 (33)ns 123 (44) 264 (104) 254 (103)*** 257 (110)

ATSP 314 (0) 314 (0)

CCSP 251 (49) 247 (45) 248 (59)

BRSP 122 (35) 131 (15)*** 123 (42) 249 (53) 247 (76)ns 248 (76)

VESP 110 (12) 110 (15) 109 (16) 251 (7) 259 (59) 260 (59)

LASP 122 (47) 123 (0) 126 (47) 243 (65) 240 (59) 241 (80)

BTSP 129 (0) 129 (0) 247 (23) 247 (23)

SAGS 286 (0) 286 (0)

LARB 241 (31) 241 (31)

SAVS 108 (47) 110 (47) 301 (36) 278 (71) 285 (71)

GRSP 248 (22) 248 (22)

SOSP 102 (33) 110 (24)ns 108 (34) 289 (58) 294 (64)*** 291 (64)

FOSP 309 (0) 309 (0)

LISP 124 (42) 136 (58)ns 127 (58) 278 (63) 276 (86)* 278 (86)

SWSP 284 (0) 301 (29) 295 (31)

WTSP 121 (19) 100 (11) 110 (36) 303 (25) 298 (23) 298 (25)

GCSP 288 (0) 288 (0)

WCSP 121 (62) 106 (69) 116 (69) 290 (56) 285 (66)*** 287 (66)

DEJU 114 (36) 124 (41)ns 115 (41) 299 (60) 299 (57)ns 297 (64)

YHBL 256 (0) 240 (7) 241 (19)

RWBL 144 (0) 116 (68) 121 (68) 300 (0) 304 (80) 295 (80)

WEME 108 (0) 108 (0) 305 (19) 305 (19)

(con.)

Table 3 (Con.)

Spring Fall

Species BNWR RGNC Combined BNWR RGNC Combined
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002 15

COGR 211 (0) 211 (0)

GTGR 151 (19) 149 (19) 211 (0) 211 (0)

BHCO 146 (29) 151 (37) 147 (37) 239 (7) 240 (16) 237 (17)

OROR 240 (16) 240 (16)

BUOR 150 (28) 155 (31) 149 (36) 239 (41) 229 (36) 230 (43)

CAFI 304 (10) 304 (10)

HOFI 124 (56) 150 (0) 130 (56) 248 (44) 282 (92) 280 (92)

PISI 285 (86) 285 (86)

LEGO 152 (22) 152 (0) 150 (22) 255 (85) 263 (80) 262 (85)

WWCR 279 (0) 279 (0)

AMGO 149 (0) 149 (0) 291 (9) 293 (83) 293 (83)

EVGR 292 (0) 292 (0)

1 Mann-Whitney U test was performed to examine the difference between the sites for each season for species with ≥ 5 capture/site/season. *,

P < 0.05; **, P < 0.01; ***, P < 0.001; and ns, not significant.

Table 3 (Con.)

Spring Fall

Species BNWR RGNC Combined BNWR RGNC Combined

Figure 3—Relationships between spring median capture date and fall median capture date at Bosque

National Wildlife Refuge of New Mexico (a) and at Rio Grande Nature Center of New Mexico (b); and

relationships between the range of spring capture date and the range of fall capture date at Bosque National

Wildlife Refuge of New Mexico (c) and at Rio Grande Nature Center of New Mexico (d).

300

280

320

260

240

220

200

Julian date (fall)

100

Julian date (spring)

120 140 160

a.

y = -1.4641x + 451.92

R2 = 0.538

300

280

320

260

240

220

200

Julian date (fall)

100

Julian date (spring)

120 140 160

b.

y = -0.9987x + 393.01

R2 = 0.4903

70

60

80

50

40

30

20

Range of capture date (fall)

20

Range of capture date (spring)

40 50 70

d.

y = 0.3x + 50.78

R2 = 0.0854

30 60

100

90

110

70

60

80

50

40

30

20

Range of capture date (fall)

20

Range of capture date (spring)

40 50 70

c.

y = 0.7513x + 32.789

R2 = 0.2098

30 60

100

90

110
16 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002

sites earlier in spring migrated through the sites later

in fall (many temperate migrants), and the species

that migrated through the site later in spring migrated through the sites earlier in fall (many Neotropical migrants). For example, the median capture date

of the Song Sparrow (a typical temperate migrant

species) was Julian date 102 in spring and 289 in fall

at BNWR and was 110 in spring and 294 in fall at

RGNC. The median capture date of the Warbling

Vireo (a typical Neotropical species) was 44 days later

than that of the Song Sparrow at both sites in spring,

and was 60 days earlier at BNRW and 58 days earlier

at RGNC in fall. Duration of a migration period for a

given species, as indicated by the range of capture

dates (the difference between last capture date and

first capture date), tended to be positively correlated

in spring and fall [r = 0.46, n = 27, P < 0.05 at BNWR

(fig. 3c) and r = 0.29, n = 27, P = 0.14 at RGNC (fig. 3d)].

In other words, species with short migration seasons

in spring (many Neotropical species) also tended to

have short migration seasons in fall.

We used the range of capture data of each species as

an indicator of the precision of migration timing. Of 33

species with ≥ 20 captures, Neotropical migrants had

more precise migration times (range of capture dates

= 43.26 ± 9.15 days, n = 23) in spring than temperate

species (range of capture dates = 55.00 ± 12.95 days, t

= 2.98, df = 31, P < 0.01). Among the 47 species with ≥

20 captures in fall, Neotropical species again tended to

have less variation of the migration timing (range of

capture dates = 66.80 ± 16.35 days, n = 30) than

temperate species (range of capture dates = 74.35 ±

18.24 days, t = 1.46, df = 45, P = 0.15).

Fat Stores and Body Mass

The birds captured during this study were dominated by low fat individuals; about 50 percent of the

individuals captured had no visible subcutaneous fat

(fat class 0); and fewer than 10 percent of the birds

were in the fat class 3 or above in both spring and fall

(fig. 4). Fat class distributions did not differ between

the two sites or between spring and fall. However,

more individuals of Neotropical migrants were in the

lower fat classes than temperate migrants in spring (G

= 13.36, df = 3, P < 0.01), while the pattern was

reversed in fall (G = 15.41, df = 3, P < 0.01).

Using species mean body mass as the sampling unit,

we found that spring birds tended to have lower body

mass than fall birds at BNWR for species captured in

both seasons (Wilcoxon test, Z = 1.68, P = 0.09), and the

pattern was reversed for the species at RGNC: spring

birds had higher body mass than fall birds (Wilcoxon

test, Z = 2.80, P < 0.01, table 4). To further test site and

seasonal effects on body mass, we performed two-way

ANOVA for 27 species for each individual species that

Figure 4—Distribution of fat classes of landbird migrants

in spring (a) and fall (b). The total number of birds

captured was 4,673 in spring and 16,291 in fall.

had ≥ 5 captures each season at each site. Nine of the

27 species (the Black-headed Grosbeak, Chipping Sparrow, Orange-crowned Warbler, Ruby-crowned Kinglet, Virginia Warbler, Western Tanager, Wilson’s

Warbler, Yellow-rumped Warbler, and Yellow Warbler) showed site and seasonal interactions in body

mass. This was because spring body mass was higher

than fall body mass at RGNC in these species, while

the pattern was either reversed or did not change in

the same species at BNWR. Nine species showed

seasonal difference of body mass. However, the trends

were not consistent among species. While six species

(the Dusky Flycatcher, Lincoln’s Sparrow,

MacGillivray’s Warbler, White-crowned Sparrow, West

Wood-Pewee, and Willow Flycatcher) had higher body

masses in spring than in fall, the body mass of the

House Wren, Spotted Towhee, and Warbling Vireo

was higher in fall than in spring (table 4).

50

40

A. 60

30

20

10

0

Percent of total captures

0 1 2 = > 3

Fat class

Neotropical

Temperate

50

40

B. 60

30

20

10

0

Percent of total captures

0 1 2 = > 3

Fat class

Neotropical

Temperate
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002 17

Table 4—Body mass (mean ± SD) of landbirds captured during spring and fall migration at Bosque del Apache

National Wildlife Refuge (BNWR) and Rio Grande Nature Center (RGNC). See table 2 for sample sizes

and appendix I for species codes.

Spring Fall

Species BNWR RGNC BNWR RGNC All P1

SSHA 161.6 127.5 ±40.5 130.1 ±39.9

COHA 302.5 ±26.9 237 ±89.1 269.8 ±65.7

AMKE 103.2 87.0 103.8 + 6.3 100.3 ±8.7

MODO 112.1 ±15.9 90.4 ±23.5 99.9 ±23.2

WWDO

YBCU 60.6 ±7.5 57.3 ±7.8 59.1 ±7.5

GRRO 284.0

CONI

BEKI 130.9 ±15.5 130.9 ±15.5

DOWO 25.5 25.6 ±2.3 25.9 ±0.4 25.2 ±1.6 25.5 ±1.5

HAWO 63.1 ±2.5 56.3 ±5.4 58.2 ±5.6

RNSA 52.4 ±2.2 41.7 ±11.2 47.0 ±9.4

WISA 43.1 47.7 45.4 ±3.3

LBWO 33.8 ±0.7 28.6 ±2.4 31.2 ±3.3

RSFL 117.5 ±23.8 186 137.0 ±13.4 132.0 ±9.5 133.8 ±13.9

OSFL 31.3 ±1.7 34 28.1 ±1.5 30.6 ±2.4

WEWP 13.2 ±1.3A 13.2 ±1.3A 12.5 ±0.9B 12.3 ±0.8B 12.8 ±1.2 ***

HAFL 9.4 ±0.5 10.3 9.8 ±0.5 9.9 ±0.7 9.8 ±0.6

LEFL 9.3 ±1.1 9.3 ±1.1

WIFL 12.6 ±1.1AB 13.1 ±1.1A 12.2 ±1.1BC 12.1 ±1.1C 12.4 ±1.1 ***

DUFL 11.3 ±1.1B 11.8 ±0.9A 10.7 ±0.9C 10.8 ±0.7BC 11.2 ±1.1 ***

GRFL 11.2 ±1.1 11.9 ±0.8 11.7 ±1.1 11.3 ±0.8 11.5 ±1.0 ns

WEFL 11.4 ±0.8 12.3 ±0.7 11.3 ±1.2 11.2 ±1.0 11.3 ±1.0

BLPH 16.8 ±1.2 16.3 ±1.1 17.0 ±1.7 17.5 ±1.5 17.0 ±1.4

SAPH 21.4 22.7 21.3 ±1.6 21.5 ±1.5

ATFL 27.3 ±2.4 26.3 ±2.4 24.5 ±1.5 26.8 ±2.4

BCFL 28.9 ±2.4 33.3 ±2.1 30.7 ±3.1

WEKI 39.5 ±1.7 35.5 ±2.8 36.1 ±2.6 36.2 ±2.6

BARS 16.0 ±0.7 17.0 ±1.5 15.1 ±1.7 15.7 ±1.1 15.8 ±1.3

VGSW 13.6 13.6

BANS 10.3 10.3

NRWS 14.2 ±1.2 13.3 15.8 14.3 ±1.2

SCJA 70.3 ±5.7 70.8 ±6.1 78.6 ±6.8 51.8 ±11.6

MOCH 11.8 10.6 ±0.6 10.9 ±0.7 10.9 ±0.7

BCCH 10.9 ±0.7 10.7 ±1.0 10.8 ±0.9

PLTI 15.5 15.5

COBU 5.6 5.5 ±0.3 5.5 ±0.3

VERD 7.4 ±0.4 7.4 ±0.4

RBNU 9.8 ±0.6 9.4 ±0.7 9.5 ±0.7

WBNU 15.8 ±3.0 17.4 ±1.2 17.4 ±1.3 17.0 ±0.8 17.0 ±1.6 ns

BRCR 7.3 ±0.9 7.4 ±0.7 7.4 ±0.8

ROWR 17.1 13.5 15.3 ±2.5

CACW 34.8 34.8

BEWR 9.6 ±0.9 10.4 ±1.04 9.6 ±0.6 9.5 ±0.7 9.6 ±0.7

HOWR 9.7 ±0.7 A 9.9 ±0.7AB 10.1 ±1.1AB 10.4 ±0.7B 10.1 ±0.8 ***

MAWR 10.2 10.8 ±0.6 10.6 ±1.1 10.7 ±0.7

WIWR 8.9 8.9

CARW 16.9 16.9

RCKI 5.8 ±0.4 A 6.3 ±0.4B 6.1 ±0.5AB 6.1 ±0.9AB 6.1 ±0.7 *

GCKI 6.0 ±0.6 6.0 6.1 ±0.5 6.0 ±0.4

BGGN 6.0 ±0.5 5.9 ±0.3 5.2 ±0.1 5.8 ±0.5

TOSO 33.9 33.0 ±2.2 33.3 ±1.6

SWTH 26.4 ±1.9 29.7 ±2.4 30.7 28.0 ±2.7

(con.)
18 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002

HETH 24.1 ±3.3 24.4 ±2.6 23.7 ±2.2 23.8 ±2.4 23.9 ±2.5 ns

AMRO 77.4 ±6.6 77.0 ±6.4 77.2 ±0.5 79.9 ±9.8 78.3 ±8.1

GRCA 33.3 ±2.6 34.6 ±2.3 35.1 ± 1.5 33.2 ± 2.7 34.3 ± 2.1

NOMO 45.2 ± 3.0 44.7 ± 3.0 44.4 44.9 ± 3.0

SATH 45.3 45.3

BRTH 75.7 75.7

CRTH 59.3 59.3

BEVI 8.8 8.8

GRVI 13.3 12.4 12.9 ± 0.6

SOVI 14.9 ± 2.2 14.6 15.0 ± 1.4 14.8 ± 2.1 14.9 ± 1.9

WAVI 11.2 ± 0.9A 11.6 ± 0.9AB 11.8 ± 1.0B 11.9 ± 0.9B 11.6 ± 1.0 ***

REVI 15.8 18.7 17.3 ± 2.1

YTVI 18 18

PROW 13.3 ± 1.3 14.1 13.6 ± 1.0

BWWA 8.0 8

LAWA 8.6 8.6

OCWA 8.8 ± 1.1A 9.9 ± 1.0B 8.8 ± 0.8A 8.7 ± 0.7A 8.8 ± 0.8 ***

NAWA 7.2 ± 1.0 8.1 7.3 ± 0.4 7.4 ± 0.7

VIWA 8.4 ± 1.0AB 8.9 ± 0.8A 8.4 ± 0.8B 8.0 ± 0.6B 8.2 ± 0.8 ***

LUWA 6.3 ± 0.3 6.3 ± 0.3

MAWA 8.4 ± 0.8 7.4 8.4 ± 0.8

YWAR 8.9 ± 0.8A 10.0 ± 0.9B 9.1 ± 1.0A 8.9 ± 0.7A 9.0 ± 0.9 ***

CSWA 8.5 ± 0.6 8.5 ± 0.6

YRWA 11.6 ± 1.2A 13.1 ± 1.3C 12.0 ± 1.1B 11.7 ± 1.1AB 12.1 ± 1.3 ***

BTYW 7.2 ± 0.2 7.6 ± 0.5 7.4 ± 0.4

BTBW 10.7 10.7

BLBW 9.7 ns

TOWA 8.5 ± 0.7 9.5 ± 0.8 8.4 ± 0.4 8.8 ± 0.8

PRAW 7.5 7.5

BAWW 8.3 9.8 ± 0.1 9.6 9.4 ± 0.7

AMRE 7.5 7.6 7.6 ± 0.4

WPWA 9.2 9.2

NOWA 18.5 ± 2.6 17.3 ± 1.7 17.5 ± 1.6 17.6 ± 1.8

OVEN 16.7 ± 1.2 17.7 ± 1.7 17.0 ± 1.4

TEWA 8.6 8.6

MOWA 11.1 11.1

MGWA 10.5 ± 1.0A 11.0 ± 1.0B 10.4 ± 1.0A 10.7 ± 0.8C 10.7 ± 1.0 ***

COYE 9.6 ± 1.0A 9.6 ± 1.2A 10.0 ± 0.8B 10.0 ± 1.0B 10.1 ± 1.3 *

KEWA 13.2 ± 1.0 13.7 13.4 ± 0.8

CAWA 8.4 8.4

WIWA 7.3 ± 0.7A 7.7 ± 0.8B 7.3 ± 0.6A 7.3 ± 0.5A 7.3 ± 0.6 ***

HOWA 10.5 ± 0.9 10.1 10.3 ± 0.7

YBCH 23.8 ± 2.2 26.2 ± 2.4 26.0 ± 2.0 28.7 ± 2.6 24.7 ± 2.6

HETA 38.1 ± 2.0 39.3 ± 0.3 38.7 ± 1.3 ns

SUTA 33.9 ± 2.3 32.8 ± 1.6 35.6 ± 3.4 34.0 ± 1.5 34.9 ± 3.1

WETA 28.9 ± 3.4 31.8 ± 1.9 29.9 ± 2.0 29.8 ± 3.1 29.8 ± 2.8 ns

SCTA 28.9 28.9

PYRR 35.1 ± 1.8 35.1 ± 1.8

RBGR 38.4 ± 5.7 40.9 39.3 39.3 ± 3.5

BHGR 43.4 ± 3.6A 45.7 ± 3.7B 45.6 ± 4.0B 43.7 ± 5.5A 44.5 ± 4.4 ***

BLGR 27.9 ± 2.1 28.3 ± 2.7 28.3 ± 2.8 28.8 ± 2.5 27.9 ± 2.5 ns

LAZB 13.6 ± 1.0 13.4 ± 1.3 14.6 ± 1.9 14.7 ± 1.4 14.7 ± 1.4

INBU 12.7 ± 0.9 14.5 ± 2.8 14.0 ± 1.4 13.8 ± 0.7 13.5 ± 1.3

PABU 15.4 ± 2.2 15.2 ± 1.1 15.3 ± 1.6

DICK 29.5 ± 5.6 29.5 ± 5.6

GTTO 27.2 ± 2.7 28.2 ± 3.0 27.6 ± 3.5 28.1 ± 2.5 28.0 ± 2.7 ns

(con.)

Table 4 (Con.)

Spring Fall

Species BNWR RGNC BNWR RGNC All P1
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002 19

SPTO 36.5 ± 2.7A 33.6 ± 2.0B 37.3 ± 3.0A 36.2 ± 2.8A 36.6 ± 2.9 **

CANT 44.0 41.4 ± 1.6 42.1 ± 1.8

CASP 15.6 15.6

CHSP 11.4 ± 1.2A 12.3 ± 0.4B 12.2 ± 0.9B 12.0 ± 0.9AB 12.0 ± 0.9 ***

ATSP 14.8 14.8

CCSP 10.3 ± 0.8 10.8 ± 0.9 10.7 ± 0.9

BRSP 10.8 ± 1.5A 10.2 ± 0.5B 10.3 ± 0.9B 10.6 ± 1.1C 10.5 ± 1.1 *

VESP 20.6 ± 1.7 23.7 ± 1.3 23.0 ± 1.2 23.4 ± 1.9 23.3 ± 1.9

LASP 28.3 ± 1.0 25.7 26.6 ± 2.6 27.2 ± 2.1 27.2 ± 2.2

BTSP 12.2 12.0 ± 0.3 12.1 ± 0.2

SAGS 18.1 18.1

LARB 33.4 ± 1.9 33.4 ± 1.9

SAVS 17.4 ± 1.7 16.4 ± 1.0 16.6 ± 1.3 16.6 ± 1.3

GRSP 16.3 ± 0.5 16.3 ± 0.5

SOSP 19.7 ± 1.4 20.0 ± 0.9 19.5 ± 1.4 19.8 ± 1.4 19.6 ± 1.4 ns

FOSP

LISP 16.2 ± 2.0 17.3 ± 1.1 16.0 ± 1.7 16.2 ± 1.7 16.1 ± 1.7 ns

SWSP 13.9 16.0 ± 0.3 15.3 ± 1.2

WTSP 26.3 ± 4.4 24.3 ± 3.0 23.1 ± 1.4 25.1 ± 1.0 24.5 ± 2.0

GCSP 29.9 29.9

WCSP 25.0 ± 2.6A 25.7 ± 2.8B 23.9 ± 2.1C 24.6 ± 2.4A 24.6 ± 2.4 ***

DEJU 18.0 ± 2.4 A 17.9 ± 1.5 17.7 ± 1.4C 17.4 ± 1.3 B 17.6 ± 1.4 ***

YHBL 75.4 47.7 ± 3.3 49.6 ± 8.1

RWBL 62.3 51.8 ± 11.6 47.6 43.1 ± 6.1 50.6 ± 11.3

WEME 132.7 92.2 ± 14.4 98.9 ± 21.0

COGR 81.5 81.5

GTGR 156.0 ± 50.6 93.0 ± 10.7 147.0 ± 51.9

BHCO 34.8 ± 4.4 33.9 ± 4.9 35.3 ± 2.4 31.5 ± 3.8 34.2 ± 4.3

OROR 19.6 ± 1.1 19.6 ± 1.1

BUOR 36.0 ± 2.5 35.0 ± 7.6 33.2 ± 2.8 34.0 ± 3.1 34.2 ± 3.2

CAFI 26.9 ± 2.2 26.9 ± 2.2

HOFI 18.8 ± 1.8 19.6 21.1 ± 1.2 19.7 ± 1.1 19.7 ± 1.2

PISI 12.3 ± 0.9 12.3 ± 0.9

LEGO 9.8 ± 0.6 9.4 9.4 ± 0.5 9.3 ± 0.6 9.4 ± 0.6

WWCR 25.7 25.7

AMGO 12.2 ± 0.9 12.8 ± 0.5 12.9 + 0.8 12.8 ± 0.8

EVGR 56.9 56.9

1

One-way ANOVA test for species with ≥ 5 captures at each site in each season. *, P < 0.05; **, P < 0.01; ***, P < 0.001; and

ns, not significant. 2

Values with same superscripts indicate no significant difference in Tukey’s multiple comparison tests among site/season

categories.

Table 4 (Con.)

Spring Fall

Species BNWR RGNC BNWR RGNC All P1

Recapture Rate, Body Condition Change,

and Length of Stay

A total of 2,870 individuals (12 percent of the total

captures) were recaptured after the day of initial

capture. Log-linear model tests suggested that recapture rates differed between the two sites (Partial

X2

 = 30.25, df = 1, P < 0.001), but not between spring

and fall. The spring recapture rate was 11 percent at

BNWR and was 12.8 percent at RGNC; the fall

recapture rate was 9.7 percent at BNWR and was 13

percent at RGNC.

Body condition is expected to affect stopover decisions. When we compared the body mass of birds that

were never recaptured (nonrecaptures) and initial

body mass of birds that were recaptured (recaptures),

we found that body mass variation was usually large

within each species, and the mass difference between

the two groups was generally small: mean mass

difference between nonrecaptures versus recaptures
20 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002

is 1.71 percent (minimum = 0.19 percent and maximum = 5.88 percent birds, table 5). The body mass of

the two groups differed in five species: body masses of

nonrecaptures of four species, Hermit Thrush (2.34

percent), Willow Flycatcher (3.83 percent), Wilson’s

Warbler (1.38 percent), and Yellow-rumped Warbler

(3.60 percent), were higher than initial body masses of

recaptures. In the Bewick’s Wren, the pattern was

reversed: body mass of nonrecaptures was 2.16 percent lower than that of recaptures. When we examined

average body mass differences between the two groups

using species as sampling units for 30 species having

≥ 20 individuals captured at each site in fall (table 5),

the initial body mass of recaptures was significantly

lower than the body mass of nonrecaptures (Wilcoxon

Singed Rank Test, Z = 3.52, P < 0.001).

Data for recaptured birds suggest that they gained

0.37 ± 1.74 g between the first capture and last capture. The percentage of their daily body mass gain

birds was 0.24 ± 2.76 percent/day. Among 2,870 recapture birds, about 29 percent showed fat class increases, 21 percent had fat class decreases, while 50

percent maintained a constant fat class between the

initial capture and last capture. The average time

between the first and last capture was 9.63 ± 12.04

days.

Forty-nine species had ≥ 5 recaptures, including 25

Neotropical, 18 temperate, and six resident species

(table 6). Based on the summary data for these 49

species, the median duration between first and last

captures differed among Neotropical (5.2 ± 3.69 days),

temperate (8.31 ± 7.33 days), and resident species

(34.76 ± 28.42 days) (F = 18.75; df = 2, 46; P < 0.001;

table 6). The percent body mass change also differed

among Neotropical (2.36 ± 2.09 percent), temperate

(0.46 ± 3.26 percent), and resident species (2.56 ± 1.77

Table 5—Body mass (mean ± SD) of nonrecaptured birds and initial body mass of

recaptured birds during spring and fall migration along the middle Rio

Grande. See appendix I for species codes.

Nonrecaptures Recaptures

Species N Body mass N Body mass t1

BEWR 101 9.51 ± 0.70 52 9.72 ± 0.81 1.62 *

BHGR 284 44.57 ± 4.57 55 44.27 ± 3.25 0.47 ns

BLGR 324 28.56 ± 2.69 97 28.2 ± 2.09 1.22 ns

BRSP 445 10.54 ± 1.14 37 10.56 ± 0.87 0.12 ns

CCSP 184 10.69 ± 0.90 7 10.59 ± 0.64 0.28 ns

CHSP 2304 12.04 ± 0.89 338 12.07 ± 0.81 0.63 ns

DEJU 664 17.56 ± 1.47 176 17.62 ± 1.26 0.47 ns

DUFL 365 11.22 ± 1.04 20 10.95 ± 1.17 1.16 ns

GTTO 130 28.02 ± 2.90 83 27.94 ± 2.48 0.22 ns

HETH 174 24.10 ± 2.66 76 23.55 ± 1.94 1.60 *

HOWR 125 10.13 ± 0.83 14 9.94 ± 0.58 0.84 ns

LASP 527 27.12 ± 2.19 17 26.91 ± 2.04 0.40 ns

LAZB 539 14.74 ± 1.46 68 14.36 ± 1.10 2.07 *

LISP 294 16.17 ± 1.77 106 15.98 ± 1.49 0.99 ns

MGWA 1006 10.71 ± 0.99 267 10.64 ± 0.90 1.03 ns

OCWA 729 8.77 ± 0.82 81 8.66 ± 0.60 1.24 ns

RCKI 550 6.04 ± 0.68 79 6.09 ± 0.61 0.61 ns

SAVS 203 16.67 ± 1.31 10 15.94 ± 1.11 1.75 *

SOSP 329 19.58 ± 1.37 96 19.72 ± 1.45 0.87 ns

SPTO 153 36.59 ± 2.84 84 36.51 ± 2.96 0.21 ns

VIWA 217 8.22 ± 0.79 25 7.98 ± 0.57 1.52 ns

WAVI 213 11.63 ± 0.98 9 11.71 ± 0.70 0.25 ns

WCSP 904 24.61 ± 2.51 504 24.46 ± 2.22 1.11 ns

WEFL 60 11.33 ± 1.02 8 10.81 ± 1.16 1.32 ns

WETA 132 29.87 ± 2.79 5 30.00 ± 2.12 0.10 ns

WEWP 206 12.78 ± 1.19 6 12.07 ± 0.85 1.46 ns

WIFL 186 12.48 ± 1.15 18 12.02 ± 0.95 1.65 *

WIWA 2997 7.34 ± 0.59 294 7.24 ± 0.56 2.85 **

YRWA 790 12.08 ± 1.31 64 11.66 ± 0.88 2.50 **

YWAR 447 9.05 ± 0.89 31 8.93 ± 0.85 0.74 ns

1.One-tail t test. *, P < 0.05; **, P < 0.01.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002 21

Table 6—Body mass change (g), percent body mass change, fat class change, and length of stay of recaptured

landbirds during spring and fall migration along the middle Rio Grande.

Mass change (g) % mass change Fat class change Length of stay

Species N Mean SD Mean SD Mean SD Median

RSFL 7 –6.95 20.30 –5.08 15.02 0.00 0.00 7.0

WEWP 7 0.43 0.90 3.71 7.95 0.00 0.58 5.0

WIFL 13 0.11 0.36 0.87 2.88 0.15 0.99 1.0

DUFL 16 0.66 1.22 6.76 12.94 0.06 0.93 2.0

BLPH 12 0.71 1.18 4.33 7.49 0.33 0.49 15.5

ATFL 6 0.75 1.77 3.03 7.02 –0.17 0.75 8.5

MOCH 13 0.26 0.54 2.44 4.99 0.08 0.76 18.0

BCCH 19 0.25 1.14 3.43 13.99 0.37 0.90 57.0

COBU 6 0.14 0.33 2.64 6.07 –0.17 0.41 81.0

WBNU 18 –0.05 1.12 –0.30 6.06 0.17 0.71 19.0

BRCR 5 –0.16 0.62 –1.79 7.53 –0.20 0.45 19.0

BEWR 49 0.14 0.81 2.06 9.75 –0.08 1.00 27.6

HOWR 13 0.13 0.54 1.40 5.59 –0.08 1.19 2.0

RCKI 63 0.01 0.54 0.52 8.90 –0.11 1.54 3.0

HETH 74 1.60 1.98 6.90 8.55 0.58 1.41 4.0

AMRO 22 –1.60 4.38 –1.74 5.34 –0.18 0.85 6.0

WAVI 8 0.18 0.87 1.70 7.64 0.63 0.74 2.5

OCWA 65 0.23 0.58 2.81 6.86 0.25 1.31 2.0

VIWA 22 0.14 0.43 1.86 5.39 0.32 0.84 2.5

YWAR 17 0.23 0.62 2.30 6.03 0.24 1.39 2.0

YRWA 60 0.40 0.87 3.56 7.36 0.42 1.09 3.0

MGWA 218 0.33 0.79 3.27 7.53 0.32 1.14 2.0

COYE 15 0.36 0.75 3.90 7.80 0.73 1.49 6.0

WIWA 216 0.09 0.52 1.42 7.50 0.15 0.94 2.0

YBCH 11 0.35 0.83 1.50 3.44 0.55 0.93 10.0

SUTA 18 0.31 1.53 0.94 4.42 0.06 0.54 13.0

WETA 6 –0.62 2.68 –1.97 9.16 –0.33 1.37 7.0

BHGR 50 1.19 3.29 2.85 7.58 0.28 1.21 8.0

BLGR 72 0.29 2.47 1.28 8.72 0.28 0.91 7.0

LAZB 68 0.63 1.37 4.56 9.42 0.38 1.52 4.0

GTTO 78 1.34 2.26 5.10 8.56 0.31 1.44 6.0

SPTO 85 0.02 2.90 0.34 7.90 –0.06 0.89 12.0

CHSP 336 0.24 0.86 2.21 7.08 0.05 1.14 11.0

CCSP 5 0.48 1.02 4.43 9.36 0.00 1.22 2.0

BRSP 38 0.37 0.89 3.71 8.14 0.21 1.02 6.5

VESP 41 0.01 1.14 0.16 4.92 –0.07 0.96 4.0

LASP 20 –0.05 1.70 0.08 6.50 0.15 0.81 1.0

SAVS 7 0.39 0.72 2.51 4.70 0.71 1.98 10.0

SOSP 77 0.60 1.15 3.06 6.29 0.10 0.74 7.0

LISP 102 0.66 1.55 4.35 9.53 0.18 1.21 8.0

WCSP 473 0.70 1.92 3.09 7.84 0.24 1.21 8.0

DEJU 149 0.23 1.03 1.43 5.77 0.01 0.82 7.0

RWBL 5 –1.28 3.07 –2.79 6.63 0.00 0.00 12.0

BHCO 8 0.73 1.79 2.46 5.61 0.13 0.35 3.0

BUOR 10 1.63 2.93 4.81 9.12 0.10 0.99 3.0

HOFI 18 –0.26 1.38 –1.12 6.88 0.22 1.00 31.0

PISI 34 –0.27 0.76 –2.02 6.12 –0.15 1.10 1.0

LEGO 19 –0.42 0.62 –4.20 6.77 –0.05 1.27 5.0

AMGO 21 –0.54 2.18 –4.07 16.88 –0.29 0.90 4.0
22 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002

percent) (F = 3.27; df = 2, 46; P < 0.05, table 6). The fat

class and body mass change between the first and last

capture was not different among the three groups.

Birds gained body mass between initial and last capture: Neotropical (0.18 ± 0.24 g), temperate (0.09 ±

0.28g), and resident species (0.11 ± 0.21g) (table 6).

The daily rate of body mass change was highest in

Neotropical migratory species (0.79 ± 0.81 percent) (F

= 5.22; df = 2, 46; P < 0.01) and did not differ between

temperate (0.03 ± 0.84 percent) and resident species

(0.19 ± 1.77 percent). The 10 species that had highest

rates of daily mass change were the Dusky Flycatcher

(3.38 percent/day), Clay-colored Sparrow (2.22 percent/day), Hermit Thrush (1.72 percent/day),

MacGillivray’s Warbler (1.64 percent/day), Bullock’s

Oriole (1.60 percent/day), Orange-crowned Warbler

(1.41 percent/day), Yellow-rumped Warbler (1.19 percent/day), Yellow Warbler (1.15 percent/day), Lazuli

Bunting (1.14/day), and Willow Flycatcher (0.87 percent/day) (table 6).

Most regression equations between body mass and

capture time for species with ≥ 20 captures/season

(two sites combined) had positive slopes, indicating

birds gained body mass during the daylight hours

when we operated the mist-nets. For example, of the

34 species examined for spring migration, the slopes of

the regression model between body mass and capture

time were positive in 29 species and were significant in

12 species (table 7); of the 50 species examined for fall

migration, regression slopes were positive in 45 species (90 percent) and were significant in 21 species (42

percent), all of which had positive slopes. The average

hourly rate of body mass changeH (regression coefficient) was 0.17 ± 0.33 g/hour in spring and 0.13 ± 0.24

g/hour in fall. This translates to a body mass increase

of 6.86 ± 9.63 percent/day in spring and 4.68 ± 0.72

percent/day in fall, assuming 8 hours available for

gaining body mass. These estimates are about 29

times (in spring) and 19 times (in fall) higher than

what were estimated based on recapture data (mean

mass change = 0.24 percent/day).

In spring, daily percent body mass change estimated

based on regression coefficients tended to be higher in

Neotropical migrant species (8.34 ± 8.53 percent, n =

23 species) than in temperate migrant species (3.57 ±

12 percent, n = 10 species). There were no differences

between Neotropical migrants (5.05 ± 4.89 percent, n

= 29 species) and temperate migrants (5.40 ± 3.01

percent, n = 17 species) in fall. However, the resident

species (-1.15 ± 5.80 percent, n = 4 species) differed

from Neotropical and temperate migrants.

Habitat Use

Capture rate was not constant among habitats in

spring (X2 = 292.01, df = 5, P < 0.001) and in fall (X2 =

807.93, df = 5, P < 0.001). In spring, capture rate was

highest in willow habitat (288 birds/1,000 net hr),

followed by agricultural field/edges (155 birds/1,000

net hr), cottonwood/Russian olive (120 birds/1,000 net

hr), saltcedar (94 birds/1,000 net hr), and cottonwood/

native understory (64 birds/1,000 net hr), and cottonwood/saltcedar (51 birds/1,000 net hr) (table 8). In fall,

capture rate was highest in agricultural field/edges

(718 birds/1,000 net hr); it was similar among cottonwood/Russian olive (286 birds/1,000 net hr), willow

(238 birds/1,000 net hr), saltcedar (214 birds/1,000 net

hr), and cottonwood/native understory (203 birds/1,000

net hr). Similar to spring migration, cottonwood/

saltcedar habitat had the lowest capture rate in fall

(97 birds/1,000 net hr). A two-contingency table test

(season by habitat type) suggests that capture rate by

habitat types varied by season (X2

 = 219.26, df = 5, P

< 0.001).

The number of species captured varied by habitats

in spring (X2

 = 42.43, df = 5, P < 0.001) and in fall (X2

= 31.38, df = 5, P < 0.001) (table 8). In spring, cottonwood/Russian olive and willow habitats had the highest species richness (80 and 79 species, respectively),

followed by agricultural field/edges (56 species), cottonwood/saltcedar (52 species), and cottonwood/native understory (38 species); and saltcedar had the

lowest species richness (26 species). In fall, cottonwood/Russian olive again had the highest species

richness (94 species), followed by agricultural field/

edges (82 species); species richness was similar among

willow (69 species), cottonwood/saltcedar (68), and

cottonwood/native understory (62). Similar to spring

migration, species richness was lowest in saltcedar

(33). In both seasons, species diversity was lowest in

saltcedar habitat but its species evenness was not

(table 8).

Capture rate was habitat-dependent in many species. For example, most warbler species had highest

capture rates in willow habitat. Among the seven most

abundant warbler species captured in spring, the

capture rates of the Orange-crowned Warbler, Virginia Warbler, Yellow Warbler, and Common Yellow

were about four to five times higher in willow than in

cottonwood/Russian olive (table 8); the Wilson’s Warbler had the highest capture rate in willow (77.37

birds/1,000 net hr), which was 11 times greater than

capture rate in cottonwood/Russian olive. Most of

these species prefer shrub and secondary growth for

breeding. On the other hand, the Yellow-rumped Warbler, a species that forages and breeds in canopies, had

the highest capture rate in cottonwood/Russian olive.

The MacGillivray’s Warbler was similar between willow (33 birds/1,000 net hr) and cottonwood/Russian

olive (29 birds/1,000 net hr). In fall, of these seven most

common species, four species, the Orange-crowned

Warbler, Virginia Warbler, MacGillivray’s Warbler,
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002 23 Table 7—Daily body mass change (% change/day) of landbirds during spring and fall migration along the middle Rio Grande of New Mexico. One day is 8 available hours for birds to forage and gain body mass. Column “a” is from y = ax + b where y is body mass at capture, x is the capture time (hr), b is the y-intercept, and a is the slope. SE is the standard error of the slope. The r measures the strength of the relationship between body mass and time, and t statistics indicate how well the linear model fits the data. % change/d is calculated as: slope x 8 hr/mean body mass of the species. The analysis was performed for species with sample size (N) ≥ 20 in each season. See appendix I for species codes. Spring Fall Species N a SE r t P %change/d N a SE r t P %change/d RSFL 43 1.58 1.14 0.21 0.14 ns 9.48 WEWP 99 0.30 0.09 0.32 3.33 ** 18.15 116 0.07 0.05 0.13 1.38 ns 4.53 WIFL 93 0.19 0.08 0.26 2.52 * 11.80 120 0.08 0.06 0.12 1.30 ns 5.29 DUFL 262 0.05 0.04 0.07 1.50 ns 3.50 136 0.10 0.04 0.20 2.34 * 7.43 GRFL 32 0.23 0.12 0.33 1.89 0.07 16.01 38 –0.20 0.11 0.29 0.18 0.08 –13.90 WEFL 63 0.05 0.09 0.08 0.61 ns 3.57 BLPH 32 0.00 0.11 0.00 0.01 ns 0.00 24 0.15 0.17 0.18 0.86 ns 6.89 ATFL 33 0.15 0.22 0.12 0.67 ns 4.45 WEKI 30 0.09 0.32 0.06 0.30 ns 1.99 NRWS 20 0.29 0.27 0.24 1.05 ns 16.36 MOCH 40 –0.03 0.07 0.08 0.48 ns –2.21 BCCH 27 –0.13 0.15 0.17 0.84 ns –9.68 COBU 31 0.01 0.05 0.03 0.15 ns 1.44 BRCR 28 0.12 0.09 0.25 1.33 ns 13.06 BEWR 35 0.08 0.09 0.15 0.86 ns 6.59 125 0.07 0.03 0.19 2.14 * 5.87 HOWR 59 0.09 0.05 0.23 1.78 0.08 7.37 94 0.11 0.04 0.28 2.74 ** 8.51 RCKI 73 0.08 0.04 0.26 2.22 * 10.76 563 0.01 0.02 0.02 0.41 ns 1.32 HETH 65 0.15 0.24 0.08 0.63 ns 4.94 189 0.09 0.11 0.06 0.85 ns 3.02 AMRO 125 0.30 0.38 0.07 0.79 ns 3.11 96 0.39 0.58 0.07 0.68 ns 3.91 SOVI 45 0.07 0.18 0.06 0.37 ns 3.76 WAVI 79 0.16 0.07 0.24 2.14 * 11.36 150 0.10 0.05 0.16 1.97 0.05 6.76 OCWA 63 0.08 0.11 0.09 0.72 ns 7.03 768 0.09 0.02 0.20 5.75 *** 8.25 VIWA 43 0.16 0.09 0.27 1.76 0.09 14.92 205 0.03 0.03 0.08 1.08 ns 2.96 YWAR 126 0.12 0.06 0.19 2.16 * 10.39 363 0.05 0.03 0.09 1.78 0.08 4.46 YRWA 256 0.10 0.05 0.11 1.84 0.07 6.32 614 0.07 0.03 0.11 2.70 ** 4.75 NOWA 26 0.34 0.36 0.19 0.94 ns 15.62 MGWA 792 0.09 0.02 0.13 3.76 *** 6.69 512 0.07 0.02 0.15 3.31 ** 5.28 COYE 129 –0.04 0.06 0.06 0.67 ns –3.32 84 0.14 0.06 0.27 2.55 * 11.16 WIWA 657 0.09 0.02 0.17 4.43 *** 9.80 2697 0.06 0.01 0.18 9.47 *** 6.56 YBCH 41 –0.18 0.25 0.11 0.72 ns –6.02 SUTA 38 0.31 0.26 0.19 1.17 ns 9.40 69 0.05 0.24 0.02 0.20 ns 1.34 WETA 124 0.31 0.15 0.19 2.12 * 8.32 BHGR 181 0.52 0.19 0.20 2.78 ** 9.35 168 –0.08 0.22 0.03 0.38 ns –1.44 BLGR 97 0.09 0.14 0.07 0.64 ns 2.57 333 0.20 0.09 0.13 2.31 * 5.59 LAZB 613 0.06 0.03 0.07 1.75 0.08 3.26(con.)
24 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002 GTTO 72 0.00 0.20 0.00 0.01 ns 0.00 153 0.21 0.13 0.13 1.62 0.1 6.00 SPTO 68 0.15 0.21 0.08 0.68 ns 3.32 183 0.05 0.09 0.08 0.61 ns 1.09 CHSP 41 0.25 0.02 0.33 2.18 * 17.32 2665 0.12 0.01 0.23 12.33 *** 7.97 CCSP 202 0.08 0.04 0.17 2.37 * 5.99 BRSP 66 0.26 0.11 0.29 2.39 * 19.44 440 0.06 0.03 0.10 2.04 * 4.56 VESP 242 0.21 0.08 0.16 2.57 * 7.19 LASP 561 0.29 0.05 0.23 5.45 *** 8.56 SAVS 23 –0.28 0.24 0.25 1.16 ns –12.87 194 0.07 0.07 0.08 1.09 ns 3.38 SOSP 432 0.19 0.04 0.22 4.56 *** 7.75 LISP 26 –0.29 0.28 0.21 1.03 ns –13.93 390 0.05 0.06 0.04 0.82 ns 2.49 WCSP 284 0.15 0.09 0.10 1.60 ns 4.75 1173 0.16 0.04 0.12 4.22 *** 5.25 DEJU 71 –0.31 0.18 0.21 1.75 0.09 –13.78 787 0.10 0.03 0.13 3.66 *** 4.56 RWBL 198 1.25 0.43 0.21 2.94 ** 19.27 40 0.20 0.55 0.06 0.37 ns 3.70 BHCO 27 1.26 0.55 0.41 2.27 * 29.11 BUOR 68 0.34 0.22 0.19 1.55 ns 8.03 HOFI 305 0.15 0.05 0.16 2.85 ** 6.09 PISI 1687 0.10 0.01 0.18 7.30 *** 6.50 LEGO 387 0.06 0.02 0.16 3.08 ** 5.13 AMGO 293 0.04 0.03 0.09 1.49 ns 2.49 1* P, <0.05; **, P < 0.01; ***, P, < 0.001; and ns, not significant. Table 7 (Con.) Spring Fall Species N a SE r t P %change/d N a SE r t P %change/d
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002 25 Table 8—Capture rate (birds/1,000 net hr) by habitat type (AGFI = agriculture field/edge, CWNA = cottonwood/native understory, CWRO = cottonwood/Russian olive, CWSC = cottonwood/saltcedar, SASC = saltcedar, and WILL = willow) during spring and fall migration. See appendix I for species codes. Spring Fall Species1 AGFI CWNA CWRO CWSC SASC WILL Total AGFI CWNA CWRO CWSC SASC WILL Total SSHA 0.00 0.00 0.00 0.14 0.00 0.00 0.03 0.00 0.21 0.50 0.00 0.00 0.00 0.20 COHA 0.00 0.00 0.13 0.00 0.00 0.00 0.06 0.18 0.00 0.14 0.10 0.00 0.00 0.10 AMKE 0.40 0.00 0.06 0.00 0.00 0.00 0.06 0.09 0.00 0.09 0.00 0.00 0.00 0.05 MODO 0.00 1.37 0.57 0.00 0.00 0.00 0.37 1.08 0.00 0.23 0.00 0.00 0.00 0.28 WWDO 0.00 0.00 0.06 0.00 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 YBCU 0.00 0.34 0.00 0.14 0.00 0.81 0.20 0.00 0.00 0.00 0.48 0.00 0.10 0.10 GRRO 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.00 0.00 0.10 0.03 CONI 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.00 0.00 0.00 0.02 BEKI 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.83 0.09 0.00 0.00 0.00 0.10 DOWO 0.00 0.00 0.32 0.14 0.00 0.00 0.17 0.00 0.62 0.36 0.38 0.00 0.00 0.25 HAWO 0.00 0.00 0.00 0.14 0.00 0.16 0.06 0.00 0.00 0.00 0.48 0.00 0.00 0.08 RNSA 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.41 0.14 0.29 0.00 0.10 0.15 WISA 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.00 0.60 0.00 0.03 LBWO 0.40 0.00 0.00 0.14 0.00 0.00 0.06 0.00 0.21 0.00 0.00 0.00 0.10 0.03 RSFL 0.00 0.00 0.06 0.14 0.00 0.16 0.09 0.00 1.03 1.31 0.86 0.00 0.00 0.71 OSFL 0.40 0.00 0.06 0.43 0.00 0.33 0.20 0.00 0.00 0.14 0.00 0.00 0.00 0.05 WEWP 3.62 2.05 2.81 1.42 0.00 4.88 2.81 2.61 2.89 2.16 1.44 0.60 0.87 1.91 HAFL 1.21 0.00 0.06 0.14 1.09 0.65 0.28 0.18 0.83 0.81 0.48 0.60 0.10 0.51 LEFL 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.09 0.00 0.14 0.00 0.00 0.00 0.07 WIFL 2.01 0.00 2.94 0.28 1.09 6.34 2.64 3.33 0.83 2.16 0.29 4.17 2.04 1.98 DUFL 1.61 3.07 5.30 6.25 15.26 17.55 7.45 2.34 1.03 3.33 1.06 4.17 1.26 2.24 GRFL 0.00 0.68 0.77 0.57 3.27 1.79 0.91 0.45 0.41 0.54 0.48 1.19 1.16 0.63 WEFL 0.00 0.68 0.19 0.28 0.00 0.33 0.26 0.81 1.03 1.53 1.15 0.60 0.19 1.04 BLPH 2.81 0.68 0.64 0.57 0.00 1.46 0.91 0.09 2.27 0.50 0.00 0.00 0.10 0.40 SAPH 0.00 0.00 0.00 0.00 0.00 0.16 0.03 0.81 0.00 0.05 0.00 0.60 0.00 0.18 ATFL 1.21 1.37 0.64 0.57 2.18 1.63 0.94 0.00 0.00 0.14 0.00 0.00 0.00 0.05 BCFL 0.40 0.00 0.00 0.14 0.00 0.16 0.09 0.00 0.00 0.00 0.00 0.00 0.19 0.03 WEKI 0.80 0.00 0.19 0.00 0.00 0.00 0.14 2.52 0.00 0.09 0.00 0.00 0.00 0.50 BARS 0.00 0.00 0.26 0.00 0.00 1.14 0.31 0.45 0.00 0.14 0.00 0.00 0.48 0.21 VGSW 0.00 0.00 0.00 0.00 0.00 0.16 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 BANS 0.00 0.00 0.06 0.00 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 NRWS 5.62 0.00 0.06 0.00 0.00 0.81 0.57 0.09 0.00 0.00 0.00 0.00 0.00 0.02 SCJA 0.00 0.00 0.32 0.00 0.00 0.00 0.14 0.00 0.21 0.36 0.38 0.00 0.00 0.21 MOCH 0.00 0.00 0.00 0.00 0.00 0.16 0.03 0.18 1.45 1.17 0.38 0.00 0.10 0.66 BCCH 0.00 0.00 0.38 0.00 0.00 0.00 0.17 0.00 1.03 0.99 0.00 0.00 0.00 0.45 PLTI 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.09 0.00 0.00 0.00 0.00 0.00 0.02 COBU 0.00 0.00 0.06 0.00 0.00 0.00 0.03 0.36 0.00 1.22 0.00 0.00 0.00 0.51 VERD 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.09 0.00 0.00 0.00 0.00 0.29 0.07 RBNU 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.32 0.38 0.00 0.19 0.21 WBNU 0.00 0.34 0.45 0.28 0.00 0.33 0.34 0.00 0.41 0.45 0.58 0.00 0.00 0.30(con.)
26 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002 Table 8 BRCR 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.62 0.59 0.96 0.00 0.19 0.46 ROWR 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.00 0.00 0.10 0.03 CACW 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.02 BEWR 0.40 2.73 0.26 1.71 3.27 1.14 0.99 1.17 1.86 2.75 1.06 3.57 2.42 2.06 HOWR 3.62 1.37 1.21 1.42 1.09 2.60 1.68 1.80 0.83 2.52 0.29 0.60 0.97 1.55 MAWR 0.00 0.00 0.06 0.00 0.00 0.16 0.06 0.18 0.00 0.14 0.00 0.00 0.29 0.13 WIWR 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.21 0.00 0.00 0.00 0.00 0.02 CARW 0.00 0.00 0.00 0.14 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 RCKI 0.80 3.07 0.96 1.14 0.00 6.34 2.07 3.69 6.81 7.52 9.21 7.14 20.76 9.29 GCKI 0.00 0.34 0.00 0.00 0.00 0.16 0.06 0.00 0.00 0.09 0.00 0.00 0.10 0.05 BGGN 0.00 0.68 0.19 0.28 0.00 0.33 0.26 0.09 0.00 0.09 0.00 0.00 0.00 0.05 TOSO 0.00 0.00 0.00 0.00 0.00 0.16 0.03 0.00 0.00 0.00 0.10 0.60 0.00 0.03 SWTH 0.00 0.68 0.00 0.28 0.00 0.33 0.17 0.00 0.00 0.09 0.19 0.00 0.10 0.08 HETH 0.40 2.73 2.62 1.14 3.27 0.65 1.85 0.36 5.58 6.30 1.25 1.19 0.29 3.12 AMRO 5.62 2.39 5.68 1.14 0.00 1.14 3.55 0.09 7.23 2.61 0.19 0.00 0.00 1.58 GRCA 0.00 0.34 0.26 0.43 0.00 0.16 0.26 0.09 0.00 0.09 0.10 0.60 0.39 0.15 NOMO 1.21 0.00 0.57 0.00 1.09 0.65 0.48 0.00 0.21 0.00 0.00 0.00 0.00 0.02 SATH 0.00 0.00 0.06 0.00 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 BRTH 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.00 0.00 0.02 CRTH 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.00 0.00 0.02 BEVI 0.00 0.00 0.06 0.00 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 GRVI 0.00 0.00 0.06 0.00 1.09 0.00 0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 SOVI 2.01 0.00 0.06 0.28 1.09 1.63 0.54 0.18 0.41 1.22 0.77 0.00 0.58 0.74 WAVI 3.21 0.68 1.28 0.71 1.09 6.99 2.24 1.35 1.86 2.93 1.06 7.14 3.69 2.48 REVI 0.00 0.00 0.00 0.00 0.00 0.16 0.03 0.00 0.00 0.00 0.10 0.00 0.00 0.02 YTVI 0.00 0.00 0.00 0.00 0.00 0.16 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 PROW 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.09 0.00 0.00 0.19 0.00 0.00 0.05 BWWA 0.00 0.00 0.00 0.00 0.00 0.16 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 LAWA 0.00 0.00 0.06 0.00 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 OCWA 2.01 0.34 1.09 0.28 2.18 5.85 1.79 28.69 3.51 16.34 0.67 2.98 5.53 12.68 NAWA 0.40 0.00 0.06 0.00 0.00 0.49 0.14 0.09 0.00 0.14 0.00 0.00 0.00 0.07 VIWA 1.21 0.00 0.89 0.14 2.18 3.74 1.22 5.04 2.07 4.01 0.86 1.79 3.69 3.38 LUWA 0.80 0.34 0.00 0.28 0.00 0.16 0.17 0.00 0.00 0.00 0.00 0.00 0.00 0.00 MAWA 0.40 0.00 0.06 0.00 0.00 0.16 0.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00 YWAR 2.01 0.68 2.62 0.00 1.09 12.52 3.58 10.70 1.03 5.31 0.38 0.00 11.35 5.99 CSWA 0.40 0.00 0.00 0.14 0.00 0.00 0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 YRWA 6.43 0.68 11.24 1.42 0.00 8.45 7.27 4.32 11.77 18.86 4.99 0.60 3.59 10.13 BTYW 0.00 0.00 0.00 0.00 1.09 0.16 0.06 0.09 0.00 0.05 0.00 0.00 0.00 0.03 BTBW 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.00 0.00 0.02 BLBW 0.00 0.00 0.06 0.00 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 (Con.) Spring Fall Species1 AGFI CWNA CWRO CWSC SASC WILL Total AGFI CWNA CWRO CWSC SASC WILL Total (con.)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002 27 Table 8 (Con.) Spring Fall Species1 AGFI CWNA CWRO CWSC SASC WILL Total AGFI CWNA CWRO CWSC SASC WILL Total TOWA 0.00 0.00 0.00 0.00 1.09 0.33 0.09 0.09 0.00 0.27 0.38 0.00 0.10 0.20 PRAW 0.00 0.00 0.00 0.00 0.00 0.16 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 BAWW 0.00 0.00 0.00 0.00 0.00 0.16 0.03 0.09 0.00 0.05 0.10 0.00 0.00 0.05 AMRE 0.00 0.00 0.00 0.00 0.00 0.16 0.03 0.09 0.00 0.00 0.00 0.00 0.10 0.03 WPWA 0.00 0.00 0.06 0.00 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 NOWA 0.00 0.00 0.45 0.00 0.00 0.00 0.20 0.09 0.62 0.63 0.10 0.00 0.68 0.43 OVEN 0.40 0.00 0.26 0.85 0.00 0.33 0.37 0.00 0.00 0.05 0.00 0.00 0.00 0.02 TEWA 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.09 0.00 0.00 0.00 0.00 0.00 0.02 MOWA 0.00 0.00 0.00 0.00 0.00 0.16 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 MGWA 14.06 13.32 28.98 6.25 15.26 33.48 22.51 11.33 4.75 10.89 2.02 3.57 9.12 8.45 COYE 5.22 0.68 1.02 0.71 0.00 15.12 3.67 1.80 0.21 0.41 0.10 0.60 5.04 1.39 KEWA 0.00 0.00 0.06 0.14 0.00 0.16 0.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00 CAWA 0.00 0.00 0.00 0.14 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 WIWA 14.06 1.37 6.83 3.84 8.72 77.37 18.67 56.39 29.74 48.79 27.93 18.45 50.44 44.51 HOWA 0.40 0.00 0.06 0.00 0.00 0.16 0.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00 YBCH 0.00 1.02 0.19 0.99 1.09 4.39 1.17 0.00 0.41 0.14 0.10 0.00 0.78 0.23 HETA 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.21 0.05 0.19 0.00 0.00 0.07 SUTA 3.62 2.05 0.32 1.28 0.00 1.46 1.08 0.27 1.86 0.14 3.84 0.00 1.36 1.14 WETA 1.21 0.00 0.45 0.14 0.00 0.65 0.43 0.09 1.65 3.33 2.30 0.60 1.55 2.05 SCTA 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.00 0.00 0.00 0.02 PYRR 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.09 0.00 0.00 0.00 0.00 0.10 0.03 RBGR 0.40 0.00 0.06 0.14 0.00 0.00 0.09 0.00 0.00 0.05 0.00 0.00 0.00 0.02 BHGR 5.62 2.73 5.36 4.55 1.09 6.83 5.14 1.98 3.30 2.84 5.76 2.38 0.29 2.77 BLGR 5.22 2.39 1.21 1.71 0.00 7.48 2.76 13.94 1.86 3.87 2.40 1.19 5.43 5.50 LAZB 0.00 0.00 0.26 0.00 0.00 0.98 0.28 43.62 0.21 4.73 0.29 0.00 1.84 10.12 INBU 0.00 0.34 0.06 0.00 0.00 1.14 0.26 0.54 0.00 0.05 0.10 0.00 0.39 0.20 PABU 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.21 0.14 0.10 0.00 0.10 0.10 DICK 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.18 0.00 0.00 0.00 0.00 0.00 0.03 GTTO 2.01 1.02 3.06 0.43 1.09 1.95 2.05 2.70 1.65 4.28 0.29 5.95 0.68 2.53 SPTO 2.81 4.10 0.51 2.70 2.18 3.25 1.93 1.17 5.58 3.60 2.21 7.74 2.62 3.02 CANT 0.40 0.00 0.00 0.00 0.00 0.00 0.03 0.00 0.21 0.09 0.00 0.00 0.00 0.05 CASP 0.00 0.00 0.06 0.00 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 CHSP 2.01 0.00 0.45 0.00 3.27 4.23 1.17 139.13 14.04 34.25 2.30 72.02 13.97 43.99 ATSP 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.09 0.00 0.00 0.00 0.00 0.00 0.02 CCSP 0.00 0.00 0.00 0.00 0.00 0.00 0.00 12.41 0.21 1.35 0.00 0.00 3.20 3.33 BRSP 2.81 0.00 0.51 0.71 16.35 5.04 1.88 19.52 0.21 5.72 0.10 6.55 8.05 7.26 VESP 1.21 0.00 0.13 0.00 0.00 0.16 0.17 15.74 0.41 2.70 0.00 0.00 0.48 3.99 LASP 0.80 0.00 0.06 0.00 0.00 0.49 0.17 39.03 0.83 3.33 0.10 0.00 4.66 9.26 BTSP 0.00 0.00 0.00 0.00 0.00 0.16 0.03 0.09 0.00 0.00 0.10 0.00 0.00 0.03 SAGS 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.02(con.)
28 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002 LARB 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.08 0.21 0.00 0.00 0.00 0.00 0.21 SAVS 0.00 0.00 1.47 0.00 0.00 0.00 0.65 9.53 0.21 1.58 0.10 0.00 4.95 3.20 GRSP 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.27 0.00 0.00 0.00 0.00 0.00 0.05 SOSP 1.21 0.00 0.45 0.00 0.00 0.65 0.40 8.72 4.34 5.67 0.29 1.79 17.65 7.13 FOSP 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.00 0.00 0.02 LISP 0.40 0.34 0.64 0.00 0.00 2.28 0.74 16.10 3.10 4.41 0.48 2.38 8.63 6.44 SWSP 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.18 0.00 0.05 0.00 0.00 0.00 0.05 WTSP 0.40 0.00 0.13 0.00 0.00 0.16 0.11 0.27 0.62 0.14 0.10 0.00 0.10 0.18 GCSP 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.00 0.00 0.00 0.02 WCSP 26.11 1.37 6.83 0.71 0.00 16.74 8.07 25.36 34.69 19.31 0.77 23.21 23.96 19.36 DEJU 6.83 2.39 0.89 1.85 2.18 2.60 1.96 4.41 16.52 18.18 11.33 27.98 8.54 12.99 YHBL 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.17 0.00 0.00 0.00 0.00 0.10 0.23 RWBL 0.40 1.37 12.32 0.00 0.00 0.00 5.63 3.24 0.00 0.14 0.00 0.00 0.10 0.66 WEME 0.40 0.00 0.00 0.00 0.00 0.00 0.03 0.45 0.00 0.00 0.00 0.00 0.00 0.08 COGR 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.00 0.00 0.00 0.02 GTGR 0.00 1.71 0.45 0.00 0.00 0.00 0.34 0.00 0.00 0.09 0.00 0.00 0.00 0.03 BHCO 0.40 0.00 0.38 0.71 0.00 2.44 0.77 0.36 0.00 0.05 0.10 0.00 0.10 0.12 OROR 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.27 0.00 0.00 0.00 0.00 0.00 0.05 BUOR 0.40 0.00 0.19 0.71 0.00 0.81 0.40 0.90 3.30 1.58 0.48 0.00 0.19 1.12 CAFI 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.54 0.00 0.00 0.00 0.00 0.00 0.10 HOFI 2.81 0.00 0.06 0.00 0.00 1.79 0.54 14.21 11.77 3.96 0.10 0.00 0.10 5.03 PISI 0.00 0.00 0.00 0.00 0.00 0.00 0.00 144.07 0.21 3.78 0.00 0.00 0.00 27.84 LEGO 1.61 0.00 0.06 0.00 0.00 0.81 0.28 31.39 0.41 1.13 0.58 0.60 0.39 6.39 WWCR 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.09 0.00 0.00 0.00 0.00 0.00 0.02 AMGO 0.00 0.00 0.00 0.00 0.00 0.33 0.06 20.86 0.00 2.57 0.00 0.00 0.39 4.84 EVGR 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.00 0.00 0.00 0.02 Evenness 0.83 0.87 0.69 0.84 0.83 0.69 0.71 0.65 0.76 0.72 0.69 0.70 0.71 0.69 All species 154.68 63.86 120.14 51.29 93.73 287.86 132.77 717.85 203.20 285.32 97.04 213.69 237.74 315.70 Spp richness 56 38 80 52 26 79 108 82 62 94 68 33 69 125 H’ 4.81 4.58 4.38 4.78 3.89 4.37 4.81 4.12 4.50 4.75 4.22 3.54 4.35 4.78 1Spp richness = total species detected; H’ = Shannon diversity index. Evenness is calculated based on Shannon diversity index (Krebs 1998) Table 8 (Con.) Spring Fall Species1 AGFI CWNA CWRO CWSC SASC WILL Total AGFI CWNA CWRO CWSC SASC WILL Total
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002 29

and Wilson’s Warbler, had highest capture rate in

agricultural habitat (table 8); capture rates were highest in willow habitat for the Yellow Warbler. Consistent with spring migration, the Yellow-rumped Warbler preferred cottonwood/Russian olive habitat for

stopover during fall migration.

Jaccard’s similarity coefficient suggested that in

spring, species composition was most similar between

agricultural habitat and cottonwood/Russian olive,

followed by cottonwood/Russian olive and willow habitats; the species similarity was low between saltcedar

and the other four habitats (table 9). In fall, bird

species composition was most similar between habitats with a cottonwood overstory component (cottonwood/native understory, cottonwood/saltcedar, and

cottonwood/Russian olive); saltcedar and all other

habitats examined were the least similar (table 9),

which was related to low species richness in saltcedar

both in spring and fall.

We selected species with capture rates ≥ 1 bird/1,000

net hr in each habitat (table 8) to perform the detrended

correspondence analyses. There were 26 species and

39 species that met this condition for spring and fall,

respectively. The distribution of a given species among

habitats was more uniform among the five habitats

studied in spring than in fall, which was suggested by

the low variation accounted for by DCA components.

The first two DCA axes of spring data accounted for 36

percent and 8 percent of the total variance, respectively. The first component tended to separate species

captured frequently in saltcedar from those species

captured more often in other habitats; the second

component tended to separate species that frequented

habitats with cottonwood overstory from species captured more often in agricultural or willow habitats

(fig. 5). The biplot suggests that the relative abundances of the Black-headed Grosbeak, Blue Grosbeak,

Chipping Sparrow, Common Yellow-throat, Dusky

Flycatcher, MacGillivray’s Warbler, Orange-crowned

Warbler, Ruby-crowned Kinglet, Virginia Warbler,

Warbling Vireo, Western Wood-pewee, Willow Flycatcher, Wilson’s Warbler, Yellow-breasted Chat, Yellow-rumped Warbler, and Yellow Warbler were the

highest in willow habitat; and the Dark-eyed Junco,

House Wren, White-crowned Sparrow were most abundant in agricultural field/edges. Habitats with cottonwood overstory were similar in species composition

and relative abundance (fig. 5).

In fall, the first two components of detrended correspondence analysis accounted for 55 percent and 17

percent of the total variance, respectively. The first

component tended to separate species that were captured most often in agricultural habitat from those

species that were captured more frequently in cottonwood/saltcedar and cottonwood/native understory

habitats (fig. 6). The second component tended to

separate species captured in willow habitat from species captured more often in saltcedar. In general,

species distribution among habitats differed between

the two seasons. While 17 of the 39 species selected for

analysis were relatively more abundant in agriculture

than in other habitats in fall, the Ruby-crowned Kinglet, Common Yellowthroat, and Yellow Warbler were

captured more frequently in willow. The Chipping

Sparrow, Dark-eyed Junco, Dusky Flycatcher, Greentailed Towhee, and Warbling Vireo were most abundant in saltcedar (fig. 6). Canopy species such as the

American Robin, Black-headed Grosbeak, Bullock’s

Oriole, Dark-eyed Junco, Hermit Thrush, Rubycrowned Kinglet, Spotted Towhee, Summer Tanager,

Western Flycatcher, Western Tanager, and Yellowrumpled Warbler, clustered along the cottonwood overstory side of the first component (fig. 6). The Brewer’s

Sparrow, Hermit Thrush, Dusky Flycatchers, and

Chipping Sparrow were grouped on the saltcedar

direction for analyses in spring (fig. 5) and fall (fig. 6).

Species that feed on seeds usually had high or highest

capture rates in agricultural habitat in fall. These

species included finches, for example, the Pine Siskin,

Lesser Goldfinch, and American Goldfinch; sparrows,

such as the Lark Sparrow, Vesper Sparrow, Claycolored Sparrow, and Savanna Sparrow; and the Lazuli Bunting (fig. 6).

Age-Related Migration

Of the 19,127 birds captured in fall, 59 percent were

hatching-year birds (immatures); 32 percent were

after-hatching year birds (adults); and the rest were

not classified because of the overlap in morphological

or developmental characteristics between immature

and adult birds. After excluding the birds of unknown

age, we detected an overall interaction between age

and site (G = 862.31, df = 1, P < 0.001). The immature/

adult ratio was about 1:1 at BNWR and was 2:1 at

RGNC. We selected 30 species, having >20 capture/

site to test effects of site on age ratio (table 10). All

species except the Savanna Sparrow showed sitedependent variations in age composition. Seven species—the Bewick’s Wren, Hermit Thrush, Yellow

Warbler, Chipping Sparrow, Savannah Sparrow,

Lincoln’s Sparrow, and White Crowned Sparrow—

had more immatures at both sites. The age structure

pattern was reversed between sites in the remaining

25 species: young birds were more abundant at RGNC

and adults were more abundant at BNWR (except the

Dark-eyed Junco) (table 10).

Two-way ANOVA (site x age) suggested that the

timing of fall migration differed between adults and

immatures in 13 of the 30 species (table 11); 12 of these

13 species were Neotropical migrants. Young birds of

nine significant species—the Willow Flycatcher, Dusky
30 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002

Table 9—Landbird species similarity (Jaccard’s similarity coefficient) among habitat types during spring and fall migration along

the middle Rio Grande Valley of New Mexico. Jaccard’s similarity coefficient (Krebs 1998) was calculated based on

birds/1,000 net hr (see table 6).

Agriculture Cottonwood/ Cottonwood/ Cottonwood/

field/edge native understory Russian olive saltcedar Saltcedar

Spring

Cottonwood/native understory 0.382

Cottonwood/Russian olive 0.581 0.405

Cottonwood/saltcedar 0.521 0.525 0.467

Saltcedar 0.344 0.333 0.293 0.345

Willow 0.552 0.427 0.574 0.523 0.313

Fall

Cottonwood/native understory 0.469

Cottonwood/Russian olive 0.586 0.592

Cottonwood/saltcedar 0.485 0.625 0.558

Saltcedar 0.369 0.439 0.337 0.443

Willow 0.573 0.578 0.583 0.593 0.417

Figure 5—Joint plot based on detrended correspondence analysis (DCA) of bird species captured

in spring along the middle Rio Grande, New Mexico. The plot shows the DCA-scores of 26 most

abundant species. Habitat type scores ± are based on bird scores (AGFI = agriculture field/edge,

CWNA = cottonwood/native understory, CWRO = cottonwood/Russian olive, CWSC = cottonwood/

saltcedar, SASC = saltcedar, and WILL = willow). See appendix I for species codes. +3.5

AGFI

-2.0

-2.5 +3.5

AMRO

RWBL

YRWA

SUTA

WEWP

GTTO

WCSP

CWRO SASC

CWNA

CWSC

WILL

YWAR

BLGR

HOWR

WIFL

MGWA

YBCH

BRSP

COYE

WIWA OCWA

VIWA WAVI

CHSP

DUFL

RCKI

BHGR

DEJU

SPTO

HETH

DCA2

DCA1
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002 31

Figure 6—Joint plot based on detrended correspondence analysis (DCA) of bird species captured

in fall along the middle Rio Grande, New Mexico. The plot shows the DCA-scores of 39 most

abundant species. Habitat scores ± are based on bird scores (AGFI = agriculture field/edge, CWNA

= cottonwood/native understory, CWRO = cottonwood/Russian olive, CWSC = cottonwood/

saltcedar, SASC = saltcedar, and WILL = willow). See appendix I for species codes.

Flycatcher, Western Flycatcher, Warbling Vireo, Orange-crowned Warbler, Black-headed Grosbeak, Blue

Grosbeak, Lazuli Bunting, and Dark-eyed Junco—

migrated through the study sites later than adults.

Young of the other four species—the House Wren,

Yellow Warbler, MacGillivray’s Warbler, and Brewer’s

Sparrow—migrated through the sites earlier than

adults (table 11).

Because body mass variation within species was

large, most species did not differ in body mass between

the two age classes. Of the 30 species examined, only

five species—the Hermit Thrush, Lazuli Bunting,

Chipping Sparrow, Lark Sparrow, and Clay-colored

Sparrow—differed in body mass by age (table 12). But

when we treated 30 species as repeated samples at

each site, we found that body mass of adult birds was

higher than that of young birds at BNWR (Wilcoxon

test, Z = 2.633, P < 0.01) and at RGNC (Wilcoxon test,

Z = 3.836, P < 0.001). The amount of observable fat was

age-dependent. Proportionally more young birds than

adults were likely to be fat-depleted (table 13). Among

4,797 adults of the 30 species, 45 percent had no

observable fat (fat class = 0), while among 8,991 young

birds, 53 percent had no observable fat reserves upon

initial capture (X2 = 96.09, df=1, P < 0.001). The

amount of fat stores was age-dependent in 13 of the 30

species. In the majority of these 13 species, adults

tended to carry more fat stores than young birds. For

example, in the Wilson’s Warblers, 66 percent of the

adult birds had fat stores while 56 percent of the young

+3.5

AGFI

-1.5

-1.5 +4.0

LEGO

LASP

LAZB

AMGO

PISI

VESP

CCSP

SAVS

YWAR

LISP

BLGR

OCWA

BRSP HOFI

CHSP

SASC

CWRO CWNA

CWSC

WILL

WCSP

HOWR WEWP

WIFL

VIWA

MGWA

WIWA

YRWA

BUOR

WETA

AMRO

HETH

WEFL

BEWR BHGR

WAVI DEJU

SPTO

DUFL

GTTO

SUTA

RCKI

SOSP

COYE

DCA2

DCA1
32 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002

birds were fat-depleted upon initial capture; in the

MacGillivray’s Warbler, 56 percent of adults carried

stored fat while only 47 percent of young birds had fat

(table 13). However, the pattern was reversed in the

Western Tanager and Lark Sparrow; proportionally,

more adults were fat-depleted than young.

The relationship between capture time and body

mass was positive in most species regardless of age,

suggesting that both age groups were able to gain body

mass during the day. Among the 30 species examined,

adults of 18 species and immatures of 19 species had

daily rates of body mass gain ≥ 4.5 percent of lean body

mass (table 14). The daily rate of body mass gain was

> 10 percent of lean body mass in adults of Chipping

Sparrow, Hermit Thrush, House Wren, Orangecrowned Warbler, Western Flycatcher, Western Tanager, and Willow Flycatcher; and in immatures of the

Blue Grosbeak, Clay-colored Sparrow, Lark Sparrow,

Song Sparrow, and Warbling Vireo. Of the 30 species

tested, the average daily rate of mass gain did not

differ between adults (6.15 percent ± 5.58) and

immatures (6.10 percent ± 4.16) (table 14).

Discussion _____________________

Species Composition and Relative

Abundance

Data presented in this report were based on mistnetting captures. Mist-netting may undersample certain species and lead to biased estimates of species

richness and relative abundance (Remsen and Good

1996, Yong and Finch 2002.). Typically, mist-netting

is an effective tool for detecting birds using habitat

within 2 to 3 m off the ground. Compared to our count

data from the same period, mist-netting tended to

Table 10—Age composition of landbirds captured during fall migration at Bosque National Wildlife Refuge (BNWR) and Rio

Grande Nature Center (RGNC). Only species with >20 captures at each site were included. AHY = after hatching

year; HY = hatching year; and U = unknown. See appendix I for species codes.

BNWR RGNC

SPP Total AHY% HY% U% Total AHY% HY% U% G1 P

WEWP 37 78 22 79 18 81 1 51.47 ***

WIFL 34 50 44 6 86 41 55 5 7.63 **

DUFL 33 64 30 6 103 20 77 3 87.76 ***

WEFL 21 52 38 10 42 26 69 5 8.13 **

BEWR 57 25 47 28 68 15 56 29 15.76 ***

HOWR 21 67 14 19 73 19 67 14 48.05 ***

RCKI 369 23 15 62 194 22 29 49 14.42 ***

HETH 33 39 52 9 156 23 72 5 13.39 ***

WAVI 64 75 25 86 49 50 1 22.01 ***

OCWA 94 45 36 19 674 26 59 15 64.51 ***

VIWA 59 73 24 3 146 36 64 1 43.48 ***

YWAR 131 48 52 232 28 71 1 40.18 ***

YRWA 105 48 39 18 509 20 65 15 52.42 ***

MGWA 145 64 35 1 367 31 66 4 60.84 ***

WIWA 1026 55 43 2 1671 26 66 9 494.76 ***

WETA 44 66 32 2 80 20 79 1 27.98 ***

BHGR 80 75 23 3 88 24 76 38.4 ***

BLGR 121 65 33 2 212 26 70 4 92.3 ***

LAZB 43 51 49 570 40 59 1 5.01 *

GTTO 22 64 32 5 131 17 76 7 24.6 ***

SPTO 79 54 44 1 104 39 58 4 24.54 ***

CHSP 392 29 59 12 2273 27 70 4 15.87 ***

CCSP 40 80 20 162 6 90 4 91.85 ***

BRSP 111 75 24 329 13 82 6 224.67 ***

LASP 63 59 37 5 498 33 67 23.63 ***

SAVS 56 41 50 9 138 34 63 3 ns

SOSP 244 50 32 18 188 26 52 22 24.06 ***

LISP 160 42 49 9 230 18 77 5 33.93 ***

WCSP 373 31 66 3 800 22 77 73.08 ***

DEJU 327 36 36 28 460 14 63 23 103.25 ***

1

Log likelihood ratio test was performed between site and age class (unknowns were excluded). ***, P < 0.001; **, P < 0.01; *, P< 0.05; and

ns, not significant.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002 33

underestimate large species such as raptors, pheasants, crows, and doves; aerial species such as swallows

and night hawks; and nocturnal species such as owls

(Yong and Finch 2002, appendix II, appendix III). In

addition to providing insight into an individual’s fitness (body condition) and population structure of species (for example, age and sex ratio), mist-netting is

more effective than other methods in detecting secretive or quiet species especially nonterritorial migrants

(Karr 1981, Remsen and Good 1996). Other researchers (for example Dunn and others 1997) have found

that intensive daily banding at stopover sites is useful

in detecting population changes over time. The technique may be especially valuable for tracking populations of species poorly monitored by breeding and

wintering counts. We believe that our mist-netting

operation was effective in achieving our objectives

given that our research focused on landbird migrants,

the majority of which were songbirds (passerines).

Mist-netting has proven to be effective in documenting

and quantifying species of this group (Sutherland

1996).

During this study, we captured 146 species in 3

years: 108 species and 133 birds/1000 net hr in spring,

and 125 species and 315 birds/1000 net hr in fall.

These figures suggest substantial use of our sites by

stopover migratory landbirds during spring and fall

migration. During their study on Appledore Island,

Maine, between 1990 and 1991, Morris and others

(1994) captured 69 species in spring and 82 species in

fall. Vega and Rappole (1994) operated 22,323 net hr

at Mcmullen and Duval Counties, Texas, capturing 59

species. The Migratory Bird Group of the University of

Southern Mississippi (Moore pers. comm.) captured

72 species on the Mississippi Gulf barrier islands

during spring migration between 1988 and 1992, and

Table 11—Median (range) of capture dates (Julian day) of adult (AHY) and hatching-year (HY) birds captured

during fall migration along the middle Rio Grande. See table 9 for species selection and appendix

I for species codes.

BNWR RGNC P1

SPP AHY HY AHY HY SITE AGE SITE X AGE

WEWP 255 (40) 254 (29) 246 (36) 248 (43) * ns ns

WIFL 243 (37) 248 (53) 232 (37) 242 (27) * ** ns

DUFL 243 (56) 263 (51) 228 (57) 253 (54) ** *** ns

WEFL 248 (61) 249 (33) 236 (33) 250 (58) ns * ns

BEWR 237 (68) 260 (89) 264 (91) 256 (80) ns ns ns

HOWR 261 (21) 254 (8) 262 (41) 254 (51) ns * ns

RCKI 276 (60) 292 (40) 268 (46) 276 (54) *

HETH 291 (43) 292 (35) 284 (50) 291 (53) * ns ns

WAVI 228 (50) 245 (36) 226 (42) 245 (61) ns *** ns

OCWA 269 (57) 273 (75) 261 (61) 265 (68) *** ** ns

VIWA 236 (68) 246 (41) 250 (50) 237 (60) *

YWAR 240 (51) 231 (47) 239 (47) 239 (53) ** ** ns

YRWA 276 (60) 280 (46) 270 (42) 271 (63) *** ns ns

MGWA 253 (45) 248 (46) 253 (63) 247 (57) ns *** ns

WIWA 255 (61) 254 (62) 255 (60) 250 (75) *

WETA 249 (58) 253 (17) 229 (37) 250 (55) **

BHGR 223 (49) 230 (55) 228 (25) 234 (52) * * ns

BLGR 242 (62) 254 (57) 240 (56) 250 (65) ns ** ns

LAZB 243 (44) 253 (21) 235 (39) 242 (52) *** *** ns

GTTO 251 (28) 248 (28) 262 (35) 259 (56) ** ns ns

SPTO 268 (59) 269 (99) 287 (45) 282 (90) *** ns ns

CHSP 270 (94) 256 (99) 231 (91) 259 (98) ***

CCSP 249 (29) 254 (49) 249 (20) 246 (45) ns ns ns

BRSP 249 (41) 243 (53) 253 (54) 246 (70) ns ** ns

LASP 243 (27) 243 (23) 229 (50) 245 (54) ***

SAVS 300 (33) 301 (32) 278 (54) 276 (69) *** ns ns

SOSP 288 (58) 289 (45) 291 (44) 293 (61) * ns ns

LISP 276 (57) 283 (63) 273 (49) 276 (86) * ns ns

WCSP 286 (56) 292 (56) 287 (56) 284 (66) **

DEJU 292 (47) 298 (53) 292 (53) 299 (57) ns ** ns

1

Two-way ANOVA was performed on rank transformed data. *, P < 0.05; **, P < 0.01; ***, P < 0.001; and ns, not significant.
34 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002

92 species along the Alabama Gulf coast during fall

migration between 1990 and 1992. Winker and others

(1992b) operated 71,398 net hr in spring and 65,799

net hr in fall at a stopover site at Washington County,

Minnesota, and captured 100 species total. At a banding site on Block Island, Rhode Island, 62 to 78 species/

year were captured during a 10-year fall migration

between 1984 and 1993 (Patton pers. comm.).

In the Western United States, the year-round banding operation at Coyote Creek Riparian Station, California, caught 90 to 102 species/year (minimum = 90

species, maximum = 102 species) between 1987 and

1993 (Shields and Katano 1994). At the Palomarin

Field Station, California, 77 species were captured

during 1996 and 1997 migrations: 64 species between

April and July and 66 species between August and

November (Point Reyes Bird Observatory 1998). During 6 years (1992 through 1997) of fall migration

banding at Creamer’s Field Migration Station, Alaska

(Alaska Bird Observatory 1998), 24,545 birds and 55

species were captured. Direct comparisons of capture

rates and species encountered at stopover sites over a

large geographic scale may be misleading owing to

variations in mist-netting operations such as daily

and seasonal netting time, length of operation, frequency of net checking, and habitats sampled. However, we believe that differences in species richness

and relative capture rates between our sites and sites

in other studies may reflect true variation of stopover

patterns across the landscape. In contrast to other

sites, our middle Rio Grande Valley sites were dominated by Western species, but were also used by

species that breed mostly in Eastern North America.

In our study, we detected many species considered

Eastern in range such as the Gray Catbird, Red-eyed

Vireo, Prothonotary Warbler, Blue-winged Warbler,

Table 12—Body mass (mean ± SD) of adult (AHY) and hatching-year birds (HY) captured during fall migration along

the middle Rio Grande. See table 9for species selection and appendix I for species codes.

BNWR RGNC P1

SPP AHY HY AHY HY SITE AGE SITE X AGE

WEWP 12.52±0.81 12.20±1.36 12.41±0.70 12.32±0.87 ns ns ns

WIFL 12.40±1.11 11.86±1.03 12.20±1.09 11.97±1.06 ns ns ns

DUFL 10.62±0.93 10.74±1.01 11.04±0.69 10.74±0.75 ns ns ns

WEFL 11.91±1.07 10.51±0.92 11.60±0.81 10.99±1.02 ns ns ns

BEWR 9.40±0.75 9.57±0.59 9.36±1.19 9.48±0.65 ns ns ns

HOWR 10.31±1.18 9.33±0.29 10.42±0.83 10.37±0.69 ns ns ns

RCKI 5.93±0.48 6.02±0.45 6.02±0.45 5.85±0.43 ns ns *

HETH 22.95±1.41 24.04±2.64 24.71±3.17 23.53±1.98 ns ** ns

WAVI 11.75±0.90 11.86±1.27 11.99±0.71 11.72±1.10 ns ns ns

OCWA 8.78±0.90 8.71±0.76 8.97±0.77 8.61±0.70 ns ns ns

VIWA 8.46±0.87 8.05±0.63 8.01±0.52 8.03±0.68 ns ns ns

YWAR 9.17±0.72 9.03±1.21 9.04±0.69 8.84±0.72 ns ns ns

YRWA 12.14±1.14 11.91±1.04 12.15±1.29 11.59±1.04 ns ns ns

MGWA 10.32±0.99 10.52±0.98 10.79±0.77 10.64±0.84 ns ns ns

WIWA 7.40±0.64 7.23±0.56 7.5±0.49 7.26±0.48 ns ns ns

WETA 29.84±2.03 29.99±2.12 29.89±3.10 29.75±3.10 ns ns ns

BHGR 45.97±3.99 44.32±4.16 43.27±5.66 43.82±5.50 ns ns ns

BLGR 28.82±2.70 27.44±2.88 30.06±2.80 28.28±2.30 ns ns ns

LAZB 14.99±2.36 14.26±1.18 15.23±1.39 14.39±1.28 ns * ns

GTTO 27.09±1.48 27.00±3.50 29.10±2.98 27.85±2.33 ns ns ns

SPTO 38.23±3.11 36.15±2.39 36.68±2.46 35.95±2.99 ns ns ns

CHSP 12.27±0.97 12.22±0.95 12.04±0.80 11.99±0.88 ** * ns

CCSP 10.22±0.78 10.39±0.86 10.95±0.94 10.77±0.89 ns ** ns

BRSP 10.27±0.91 10.29±0.86 10.99±1.62 10.55±1.00 ns ns ns

LASP 27.07±2.23 26.32±2.14 27.68±2.06 26.93±2.12   ns

SAVS 16.37±1.09 16.39±0.98 16.39±0.94 16.64±1.39 ns ns ns

SOSP 19.55±1.31 19.30±1.36 19.64±1.59 19.76±1.35 ns ns ns

LISP 16.15±1.53 15.85±1.62 16.65±1.56 16.02±1.64 ns ns ns

WCSP 24.30±2.27 23.80±1.94 25.00±2.48 24.28±2.34 ns ns ns

DEJU 17.79±1.40 17.74±1.51 17.74±1.51 17.36±1.23 ** 

1

Two-way ANOVA was performed on rank transformed data. *, P < 0.05; **, P < 0.01; ***, P < 0.001; and ns, not significant.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002 35

Lawrence’s Warbler, Tennessee Warbler, Kentucky

Warbler, Magnolia Warbler, Chestnut-sided Warbler,

Blackburnian Warbler, Prairie Warbler, Black-andWhite Warbler, American Redstart, Northern Waterthrush, Ovenbird, Mourning Warbler, Canada Warbler, Nashville Warbler, Hooded Warbler, Scarlet

Tanager, Painted Bunting, Dicksissel Orchard Oriole,

Rose-breasted Grosbeak, and White-throated Sparrow. Previous to our study, Kentucky Warbler, Mourning Warbler, Swainson’s Thrush, and Red-eyed Vireo

were not detected at BNWR. Magnolia Warbler, Palm

Warbler, Lawrence’s Warbler, and Cassin’s Sparrow

were not previously observed at RGNC.

About 85 percent of the 144 species and over 98

percent of the individuals captured in this study were

migratory. The majority are Neotropical-Nearctic

migratory species. Riparian habitat along the middle

Rio Grande may create a population bottleneck for

these species during migration if limited resources

dictate time to refuel. Because migratory birds may be

vulnerable to navigational mistakes, starvation, and

predation risk, the probability is high that migration

for some species will be altered or disrupted in response to disturbances and habitat changes (Yong and

Finch 1997b).

There were inconsistencies of the relative abundances estimates between our study and data from

Hink and Ohmart (1984) and Hoffman (1990). Disparities among the three studies could be the result

of variation in: (1) sampling techniques, (2) time

period sampled, (3) habitat types sampled, (4) sampling locations, and (5) population changes among

Table 13—Fat condition by age class (AHY = adults, HY = hatching-year immatures) of fall

migrants along middle Rio Grande. X2

 test was performed on the frequency

distribution of age by fat class (Lean = no observable fat, and Fat = with observable

fat) for each species. See table 9 for species selection and appendix I for species

codes.

AHY HY

Species N Lean Fat N Lean Fat x2 p <

WEWP 43 51% 49% 72 68% 32% 3.25 0.07

WIFL 52 56% 44% 62 63% 37% 0.60 ns

DUFL 42 52% 48% 89 63% 37% 1.32 ns

WEFL 22 55% 45% 37 65% 35% 0.62 ns

BEWR 24 58% 42% 65 69% 31% 0.93 ns

HOWR 28 39% 61% 52 40% 60% 0.01 ns

RCKI 127 32% 68% 111 30% 70% 0.09 ns

HETH 49 41% 59% 130 51% 49% 1.41 ns

WAVI 90 36% 64% 59 32% 68% 0.18 ns

OCWA 217 46% 54% 433 52% 48% 2.50 ns

VIWA 95 59% 41% 107 59% 41% 0.00 ns

YWAR 128 56% 44% 233 64% 36% 2.06 ns

YRWA 149 42% 58% 371 53% 47% 5.35 *

MGWA 205 44% 56% 292 58% 42% 8.78 **

WIWA 997 34% 66% 1532 56% 44% 112.40 ***

WETA 45 53% 47% 77 35% 65% 3.90 *

BHGR 81 36% 64% 85 48% 52% 2.63 ns

BLGR 135 61% 39% 188 78% 22% 10.83 **

LAZB 248 32% 68% 358 37% 63% 1.37 ns

GTTO 36 33% 67% 107 40% 60% 0.54 ns

SPTO 83 76% 24% 95 73% 27% 0.25 ns

CHSP 723 49% 51% 1800 50% 50% 0.33 ns

CCSP 41 24% 76% 154 40% 60% 3.24 0.07

BRSP 126 37% 63% 295 48% 52% 4.29 *

LASP 200 44% 56% 355 27% 73% 16.01 ***

SAVS 54 50% 50% 130 72% 28% 7.80 **

SOSP 171 66% 34% 174 71% 29% 5.01 *

LISP 108 58% 42% 255 64% 36% 1.19 ns

WCSP 295 46% 54% 866 57% 43% 11.75 **

DEJU 183 53% 47% 407 62% 38% 4.63 *

All birds 4797 47% 53% 8991 54% 46%

1

*, P < 0.05; **, P < 0.01; ***, P < 0.001; and ns, not significant.
36 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002 Table 14—Comparison of daily body mass change (% change/day) of adult and hatching-year landbirds during fall migration along the middle Rio Grande. One day is 8 available hours for birds to foraging and gaining body mass. Column “a” is from y = ax + b where y is body mass at capture, x is the capture time (hr), b is the y-intercept, and a is the slope. Lean mass is the average body mass of “0” fat individuals captured during the study. % change/d is calculated as: slope x 8 hr/lean mass. See table 9 for species selection and appendix I for species codes. Adults Immatures Species N b a Gain/d Lean mass %gain/d N b a Gain/d Lean mass %gain/d BEWR 21 8.33 0.11 ns 0.88 9.50 9.26 61 9.03 0.05 ns 0.4 9.51 4.21 BHGR 76 44.27 0.11 ns 0.88 44.15 1.99 81 45.11 –0.13 ns –1.04 42.19 –2.47 BLGR 131 28.60 0.09 ns 0.72 28.30 2.54 184 24.50 0.40   3.2 28.06 11.40 BRSP 124 10.30 0.02 0.16 10.53 1.52 281 9.85 0.07 **  0.56 10.12 5.53 CCSP 39 11.06 –0.07 –0.56 10.18 –5.50 148 9.45 0.14*** 1.12 10.31 10.86 CHSP 699 10.72 0.15   1.20 11.82 10.15 1770 10.96 0.11 ** ** 0.88 11.84 7.43 DEJU 180 17.05 0.06 0.48 17.45 2.75 402 16.12 0.14   1.12 17.25 6.49 DUFL 40 9.72 0.13 1.04 11.06 9.40 86 9.72 0.11 **  0.88 10.63 8.28 GTTO 33 25.98 0.26 2.08 26.34 7.90 106 26.42 0.14 1.12 26.67 4.20 HETH 47 21.16 0.36 2.88 23.09 12.47 126 23.41 0.02 0.16 23.01 0.70 HOWR 25 8.97 0.16 * 1.28 9.61 13.32 47 9.29 0.11 ** 0.88 9.88 8.91 LASP 193 25.91 0.18 * 1.44 27.19 5.30 343 23.49 0.36 *** 2.88 26 11.08 LAZB 241 13.89 0.15 ** 1.20 14.51 8.27 347 14.02 0.04 0.32 13.69 2.34 LISP 102 16.22 0.01 0.08 16.14 0.50 249 15.19 0.09 0.72 15.58 4.62 MGWA 197 9.47 0.12 *** 0.96 10.35 9.28 287 10.17 0.05 * 0.4 10.45 3.83 OCWA 212 7.83 0.12 **** 0.96 8.73 11.00 420 7.63 0.10 **** 0.8 8.33 9.60 RCKI 124 5.40 0.06 * 0.48 5.79 8.29 110 5.69 0.02 0.16 5.66 2.83 SAVS 53 17.22 –0.09 * –0.72 16.64 –4.33 127 15.18 0.15 * 1.2 16.42 7.31 SOSP 163 18.99 0.07 0.56 19.40 2.89 169 16.87 0.30 **** 2.4 19.52 12.30 SPTO 80 37.67 –0.02 –0.16 36.61 –0.44 88 33.47 0.27 * 2.16 35.95 6.01 VIWA 92 7.99 0.03 0.24 8.16 2.94 104 7.66 0.04 0.32 7.89 4.06 WAVI 87 11.48 0.04 0.32 11.15 2.87 54 10.06 0.18 * 1.44 11.09 12.98 WCSP 283 23.07 0.18 ** 1.44 24.26 5.94 836 22.80 0.16 * 1.28 23.8 5.38 WEFL 19 9.61 0.24 1.92 11.44 16.78 36 10.51 0.04 0.32 10.85 2.95 WETA 44 25.27 0.49 ** 3.92 29.05 13.49 75 27.49 0.24 1.92 28.87 6.65 WEWP 42 12.05 0.05 0.40 12.58 3.18 69 11.47 0.09 0.72 12.2 5.90 WIFL 48 10.85 0.17 1.36 12.22 11.13 59 11.39 0.06 0.48 11.83 4.06 WIWA 982 6.83 0.07   0.56 7.19 7.79 1503 6.69 0.06 ** ** 0.48 7.11 6.75 YRWA 146 10.95 0.12 ** 0.96 11.60 8.28 363 10.99 0.06 * 0.48 11.15 4.30 YWAR 126 8.61 0.06 0.48 8.81 5.45 226 8.47 0.05 0.4 8.75 4.57 1P, ≤ 0.1; ** P, <0.05; ***, P < 0.01; ****, P, < 0.001; and ns, not significant.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002 37

study time periods. We used mist-netting to sample

stopover migrants while the other two studies used

transect surveys. Mist-netting, as noted earlier, tends

to be more effective at detecting rare or secretive

species than point counts or transects. The other two

studies also surveyed birds less frequently than ours

did. While our banding stations were located at two

sites along the Rio Grande, Hink and Ohmart’s (1984)

study encompassed 163 river miles between Espanola,

at the south end of the Rio Grande Gorge, and the San

Acacia Constriction, in north-central New Mexico, and

used 78 (each 600 m x 30 m) transects. Hoffman’s

(1990) study was restricted to State parks in the

middle Rio Grande Valley.

Migration Route

If migrants use the same routes for both spring and

fall migrations, capture rates at a single stopover site

would be higher in fall than in spring because of the

contribution of hatching-year birds (Winker and others 1992b). In our study, fall captures (75 percent of 30

species) exceeded spring captures, although in 25

percent of the species spring captures were greater

than fall captures. Several alternative hypotheses

may explain this deviation: (1) species used different

migration routes in spring and fall, (2) breeding success was low for some species, or (3) age groups differed

use of migration routes in fall. Species with lower fall

captures (fall captures/spring captures <1) were the

Red-Winged Blackbird (0.20), Dusky Flycatcher (0.52),

MacGillivray’s Warbler (0.65), Common Yellowthroat

(0.65), Black Phoebe (0.75), American Robin (0.77),

and Black-headed Grosbeak (0.93). The ratio of fall

capture to spring capture was high in the Chipping

Sparrow (65.00), Lincoln’s Sparrow (15.00), Orangecrowned Warbler (12.19), and Dark-eyed Junco (11.08).

Given that these species usually have one to two

broods per breeding season and about four eggs per

brood (Ehrlich and others 1988), and given our intensive sampling, we suggest that these species may use

different routes for spring and fall migrations. The

capture rates of three Fringillidae species, the Pine

Siskin, Lesser Goldfinch, and American Goldfinch,

were biased toward fall migration. While we never

captured the Pine Siskin in spring at either site in any

year during the study, 1,687 individuals were captured during fall migration; only two American Goldfinches were captured in spring and 293 birds were

captured in fall. Intraspecific changes in migration

route between spring and fall is the most probable

explanation. Seasonal variation in available resources

is another factor that may influence fall capture rate.

For example, agriculture fields were loaded with corn,

sunflowers, and insects in the fall (Yong and Finch

pers. obs.) and not in the spring; and the capture rates

in these fields were much higher in fall than in spring.

During the first spring operation, we decided not to

mist-net at RGNC fields in the spring because we

hardly captured any birds there.

To better understand seasonal migration routes and

landscape scale associations, future researchers should

investigate migrant use of nonriparian habitats. Such

research will provide more complete data for describing migration routes of different species, will clarify

how and when migratory populations are limited, and

hence help in designing effective management and

conservation plans.

Migration Timing

Spring migration peaked in late April and the first

2 weeks of May while fall migration peaked after the

last week of August and through mid-October. Given

high relative abundances and species richness, demands for stopover resources along the middle Rio

Grande were greatest during peak migration periods.

By examining seasonal passage of time, we gained a

general idea of the time species allocated to breeding

grounds, wintering grounds, and stopover sites. Temperate migrants and Neotropical species spent different amounts of time on the breeding grounds. The

difference between spring and fall median capture

date for temperate migrants was 163 days, suggesting

that these species spent about 45 percent of their

annual life on the breeding grounds. The difference for

Neotropical species was 115 days, suggesting these

birds spent only 32 percent of their annual life on

breeding grounds.

Our analyses suggest that Neotropical migrants

spent about 45 percent of their annual life cycle away

from their wintering grounds (33 percent for breeding

and 12 percent for migration) while temperate migrants spent about 63 percent of their annual life

away from their wintering grounds (45 percent for

breeding and 18 percent for migration). Eight Neotropical species—the Western Wood-Pewee, Willow Flycatcher, Warbling Vireo, Yellow Warbler, MacGillivray’s

Warbler, Summer Tanager, Black-headed Grosbeak,

and Blue Grosbeak—spent less than 30 percent of

their annual life cycle on the breeding grounds. Winker

and others (1992b,c) found a similar pattern in Minnesota among Neotropical migratory species, although

the sampled species were mostly Eastern breeders.

Because Neotropical migrants are more constrained

by time for breeding than temperate migrants, adverse effects of disturbances at stopover sites or on

breeding grounds may have greater influence on breeding chronology, breeding success, and population dynamics of Neotropical migrants than of temperate

migrants.

Comparing temperate to Neotropical migrants is

difficult for two primary reasons. First, some temperate migrants have not finished fall migration by the
38 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002

beginning of November and have started spring migration by early April. Therefore, the capture data

from our sites do not encompass the migration period

entirely for temperate species. Second, some temperate species winter in the study area, and thus it would

be difficult to separate stopover and winter individuals in these species. However, because our calculation

of percentage of an average temperate migrant’s annual cycle on the breeding grounds was based on a

later starting date in spring and on an earlier finishing

date in fall than what would be desired, we would

underestimate the actual time that temperate migrants spent on the breeding grounds. Alternatively

temperate migrants moved more slowly and spent 13

percent more time in migration after they left our

sites.

Fat Stores, Body-Mass Change, and

Stopover Length

More than 50 percent of the birds we captured had

no fat stores, and only 10 percent had moderate or

large fat stores (fat class ≥ 3), which suggests that

landbirds migrating through our sites do not carry

large fat loads, and hence, they may need to restore fat

periodically en route. Body mass was variable between sites and seasons depending on species. Neotropical migrants had generally fewer fat stores than

temperate migrants during spring migration.

Recapture rates varied widely depending on species.

The overall recapture rate of 12 percent was, however,

comparable to other studies. For example, Morris and

others (1996) reported a recapture rate of 13.4 percent

on Appledore Island in Maine. Yong and Moore (1997)

indicated a recapture rate of 14 percent for stopover

thrushes in spring along the northern coast of the Gulf

of Mexico. Migrants were more likely to be recaptured

in fall (13 percent) than in spring (10 percent), suggesting that fall migrants may be slightly less timeconstrained. This pattern is consistent with results

reported by Morris and others (1994) and Winker and

others (1992a).

Stopover length may depend on energetic condition—

that is, availability of fat stores—upon arrival at the

stopover site (Bairlein 1985, Moore and Kerlinger

1987, Yong and Moore 1997). Our results support this

idea. Intraspecific body mass at initial capture of

nonrecaptured birds was higher than initial mass of

recaptured birds, suggesting that lean birds are more

likely to stay overnight at our sites. Body-mass differences between recaptured and nonrecaptured birds

were usually small but apparently sufficient enough

for birds to base stopover decisions on. This pattern

was also detected among spring landbird migrants

along the northern coast of the Gulf of Mexico (Moore

and Kerlinger 1987, Yong and Moore 1997). A migrant

may use incremental changes in its energetic condition to help weigh the cost of finding and settling at a

new stopover site against the benefit of staying and

refueling at the current site (Alerstam and Lindström

1990). Overall migration speed may be maximized

through a migrant’s ability to gauge such costs and

benefits. In addition, carrying less fat may be less

costly for some migrants than stopping periodically to

redeposit fat along the way. Given the continuity and

north-south direction of riparian habitat in the middle

Rio Grande where we were mist-netting, the advantage for a migrant of flying nonstop through this area

carrying large fat stores may be less than the advantage of flying light and stopping periodically to refuel.

Energetic status may not be the only contributing

factor influencing stopover decisions. Migratory distance may affect stopover patterns. Safriel and Lavee

(1988) suggested that stopover length of migratory

birds is sensitive to time constraints during passage.

Alerstam and Lindström (1990) proposed a time-optimal model for stopover migrants. We suggest that

migration time is a greater constraint in spring than

fall because of the need to synchronize arrival time

with the period when weather and food supplies are

suitable for successful nesting. In our study, Neotropical migratory species not only differed in temporal

migration patterns, they also tended to have lower

recapture rates, shorter stopover periods, and higher

rates of body-mass gain than temperate migrants,

especially in spring. Neotropical migratory species

have typically traveled longer distances than temporal migrants by the time they are captured at our sites.

To ensure sufficient time for breeding activities, short

stopover lengths and fast migrations are more likely to

be favored in long-distant migrants than in temperate

migrants.

Although migrants face many obstacles during migration, insufficient energy stores may be the most

critical barrier limiting migration. Lindström (1991)

found that maximum daily gains of recaptured birds in

his study were between 4 to 5 percent body mass for

small passerines. Winker and others (1992a) and

Winker (1995) suggested that a daily gain of 4.5

percent body mass was needed to offset overnight

losses. If birds are depositing fat, diurnal gains should

exceed overnight losses, resulting in a net positive

daily (24 hour) gain (Winker and others 1992a). When

energetic reservoirs cannot be replenished during stopover to the level needed to compensate for the cost of

delayed migration produced by stopping, then stopover sites could potentially serve as “sink” habitats

that reduce migrant fitness for individuals that stop.

In our study, the average rate of diurnal body mass

gain was 7.46 percent in spring and 4.68 percent in

fall, exceeding rates reported for other areas. By offering stopover sites that allow rapid mass regain, the
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002 39

middle Rio Grande bosque may be especially valuable

for migrants that need to stop en route to feed, gain

mass, and resume migration quickly.

Our data demonstrated that spring birds captured

at our study sites were able to accumulate body mass

(fat stores) faster during the day than fall birds.

Several hypotheses may explain why fall birds gained

mass at a somewhat slower rate than spring birds: (1)

migration timing may be more flexible in fall than in

spring leading to reduced selective pressure for short

stopovers and quick body mass gain in fall, (2) a large

proportion (about 60 percent) of fall migrants are

hatching-year birds, unable to forage and replenish

fat stores as efficiently as adults (Burger 1988,

Wunderle 1991), or (3) the presence of large numbers

of migrants—adults plus young—at fall stopover sites

may magnify the competition potential among individuals, forcing some birds to use less favorable habitat, resulting in lowered rates of mass gain. These

hypotheses may be interdependent. Our research on

the Wilson’s Warbler (Yong and others 1998) suggests

that variation in fall stopover patterns was age-related. Young Wilson’s warblers had lower fat stores,

higher recapture rates, and longer stopovers than

adults. Future in-depth analyses of other migratory

species captured during our study will help to clarify

factors that influence stopover ecology of Rio Grande

migrants in spring and fall.

The rate of body-mass change estimated from recaptured birds was lower than the rate derived from

linear regression analysis of body mass and initial

capture time, a pattern characteristic of landbird

stopover studies (Winker and others 1992a, Morris

and others 1996, Yong and others 1998). Rates of mass

gain based on recaptures may underestimate actual

rates. Recaptured birds usually represent only 10 to

15 percent of the total birds captured at stopover sites.

Recaptured birds typically have lower fat stores than

birds that were never recaptured and may represent a

subset of migrants that take longer to restore mass.

Small sample sizes and atypical fat conditions of

recaptured birds may bias estimates of stopover length

and rate of mass gain. However, the rates of mass gain

estimated based on the slopes of the regressions between body mass and capture time may overestimate

the mass change because many migrants were probably rehydrating after nocturnal flight and therefore

appeared to be gaining mass more rapidly than they

actually were.

Habitat Use

Migrant species composition varied by habitat in

spring and in fall. Because standard mist-netting can

sample a vertical stratum of about only 2 to 3 m high,

interpretations of habitat use based on capture rates

should account for the potential to undersample canopy

birds (Yong and Finch 2002). To compensate for this

potential bias (1) we established 6-m “high nets” in

habitats with cottonwoods, (2) we standardized mistnetting procedures (Ralph and others 1993) to control

variation in netting samples among days, seasons,

and years, (3) we sampled available habitats at study

sites concurrently, and (4) we operated mist-nets

through entire migration periods rather than truncating seasons as capture rates decreased. We also compared spring and fall capture rates by habitat to

evaluate whether differences in capture rates may

reflect changes in stopover habitat selection by season. However, capture rate could also be affected by

other confounding factors such as net effort variations

among habitats. For example, we added 20 additional

mist-nets (14 at willow stretch along a irrigation

channel and six at an adjacent saltcedar site) at

BNWR. The addition of these nets could bias our

comparison of species richness and relative abundance among habitats. More detailed analysis such as

comparisons using rarefaction method (Krebs 1998) is

needed. Our analyses and discussions about habitat

related variations are intended to provide a general

description.

Cottonwood/Russian olive understory habitat was

used by more migrant species than other habitats in

spring (80 species) and fall (94 species). Saltcedar was

used by fewest species (spring - 26 species, fall - 33

species). Cottonwood/Russian olive habitat at our sites

comprised mature or mixed age classes with foliage

present in all vertical layers similar to the dense

foliage and foliage height diversities of this habitat

observed by Hink and Ohmart (1984). Cottonwood

reached 15 to 18 m in height, and Russian olive was

present as a shrub or small tree. High foliage volume

and structural variation of cottonwood/Russian olive

sites may attract migrants in need of rest and food

because such habitats may signal abundant or diverse

food resources and cover. Willow and saltcedar habitats had lower canopies and forage diversity than

cottonwood/olive habitat and were usually dense, especially monotypic stands of saltcedar. Willow habitats were used as stopover sites by many insectivore

passerines such as warblers. Russian olive and

saltcedar are invasive, nonnative species that have

altered successional stages, physical structure, and

species composition of plant communities in the middle

Rio Grande (Campbell and Dick-Peddie 1964). Although our study indicates that migrants use stopover

habitats containing Russian olive as an understory,

the role of Russian olive in providing stopover resources is unclear.

Although saltcedar habitat in the Southwest has

been reported to have lower species richness than

native riparian vegetation (Anderson and others 1977,
40 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002

1978, Engel-Wilson and Ohmart 1978), Ellis’s (1995)

study in the middle Rio Grande found that bird species

richness did not differ between cottonwood and

saltcedar in migration and breeding seasons. Thompson and others (1994) and Leal and others (1996)

suggest that saltcedar and Russian olive mixed with

native plant species provide valuable current structure that was historically supplied by cottonwoodwillow communities. Discrepancies among studies in

the extent to which migrants use exotic habitats may

be related to local differences among study sites or to

differences in sampling techniques. Overall species

richness in Ellis’s (1995) study was lower than ours,

and many migrant species were not detected. Ellis

(1995) observed 18 species in saltcedar in spring and

27 in fall; in cottonwood, she detected 27 in spring and

31 in fall. Several species that call at low frequencies

were detected during our study but were not recorded

by Ellis. We suggest that differences in sampling

methods explain differences in species richness and

composition between the two studies.

Ellis (1995) suggested that certain components of

mature, native forest must be important to specific

bird species during spring migration given their restricted use of cottonwood habitats. In our study,

several migrant species such as the Dusky Flycatcher,

Gray Flycatcher, Bewick’s Wren, Chipping Sparrow,

and Brewer’s Sparrow were captured more frequently

in saltcedar than in other habitats regardless of spring

or fall season, whereas other species were captured in

saltcedar more often than in other vegetation in only

one but not both seasons. For example, Ash-throated

Flycatcher and Hermit Thrush were captured more in

saltcedar than in other habitats in spring, whereas the

Warbling Vireo and Green-Tailed Towhee were captured more in saltcedar than elsewhere in fall.

The Willow Flycatcher, an endangered species in the

Southwest, was captured more frequently in saltcedar

than in other habitats during fall migration. In the

Southwestern United States, the Willow Flycatcher is

a riparian-obligate species. The Southwestern Willow

Flycatcher is endangered owing to loss of native vegetation along water ways (USFWS 1995). Use of

saltcedar by willow flycatchers during migration may

represent an adjustment to this introduced plant species or may simply reflect the limited availability of

suitable native vegetation such as coyote willow. Willow Flycatchers are known to use saltcedar as nesting

substrate (Muiznieks and others 1993), and insect

food resources may be sufficiently abundant in

saltcedar to support nesting flycatchers (Mark Sogge

unpubl.) The presence of scattered native plants in

areas dominated by saltcedar may greatly enhance

the value of saltcedar habitat to birds (Anderson and

others 1977, Raitt and others 1980, Ellis 1995).

Using indices of species richness and relative abundance alone to assess suitability of stopover habitat

could be misleading. For example, our studies of stopover Willow Flycatchers (Yong and Finch 1997a) and

Wilson’s Warblers (Yong and others 1998) suggest

that differences in capture rates among habitats do

not necessarily reflect variation in migrant energetic

conditions (fat stores) among habitats. To better understand the relative value of different habitats to

migrating landbirds, experimental manipulations of

habitat that can be linked to changes in fitness are

required.

Age-Related Differences in Capture Rates

More than 60 percent total migrants in fall are

young birds. Whether fall migrations by young birds

are successful or not in any given year can directly

affect the annual population status of the species.

Young birds typically forage less efficiently than adults

(Wunderle 1991) and may be less efficient at extracting energy and nutrients (Karasov 1990). These factors may constrain their ability to build up fat stores

during migration. Young birds are also often socially

subordinate to adults during the nonbreeding season

(Gauthreaux 1978, Terrill 1987), which could be disadvantageous to hatching-year migrants if status limits foraging opportunity or foraging efficiency and

consequent deposition of energy stores. Subordinate

status and lack of experience could limit migration

success of young birds especially given the heightened

energy demand for long-distance migration. Studies of

age-related stopover ecology may be useful in detecting habitat deterioration if young birds respond with

greater sensitivity to habitat disturbance.

To minimize predation risk and orientation errors,

and maximize habitat use and survivorship during

migration, immatures might be expected to migrate

with their parents. Natural selection may favor

immatures that follow their migrating parents in fall

because parents have greater experience than young

in selecting stopover habitats, avoiding predators,

setting direction, and negotiating terrain. Timing of

adult and immature migrations varied, however, by

species and by site. In some species, adults migrated

through our banding sites earlier in fall than young

birds, but in other species, the pattern was reversed.

For the majority of species in our study, however, agedependent differences in migration timing were not

detected. Stewart and others (1974) observed that

adults migrated earlier than immatures in fall, while

Nilsson (1970) found that immatures migrated earlier

than females. Murray (1966) and Lebeman and Clench

(1970) reported that adults and immatures of many

Eastern passerines traveled at approximately the
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002 41

same time in fall. Inconsistencies in age-related timing among species and studies could be related to

stopover location along migration routes. Variations

in migration distance, wintering locations, breeding

and molting chronology, and social dominant status

between age classes will influence migration times

within each age class and obscure differences between

age classes. Inconsistency may also be an artifact of a

statistical parameter (median date) used for measuring migration timing. For a better understanding of

migration timing of adult and young birds, monitoring

offspring and their parents along the migration route

is needed.

Age structure (immature versus adult ratio) is also

useful in interpreting migration patterns. Many studies report differences in composition of immatures and

adults among sites at different geographical locations

(Drury and Keith 1962, Murray 1966, Ralph 1981,

Stewart and others 1974, Winker 1995, Morris and

others 1996). In our study, about 48 percent of the

birds captured during fall migration were hatchingyear birds, and 45 percent were adults. The resulting

1:1 ratio of immature and adults in our study is lower

than ratios reported at other locations. For example,

immatures typically represented over 80 percent of

Atlantic Coast (Drury and Keith 1962, Murray 1966,

Ralph 1981, Morris and others 1996) and Pacific Coast

migrants (Ralph 1971, Stewart and others 1974). At

Block Island of Rhode Island, over 90 percent of the

fall-migrating birds of most species were immatures

(Reinert pers. comm.). However, Winker (1995) reported that immatures only accounted for 28 percent

of American Redstarts and 37 percent of Magnolia

Warblers captured in southern Veracrus, Mexico. Low

ratios of immatures to adults at a given stopover site

could be linked to (1) low breeding success, (2) high

mortality of migrating immatures prior to arrival at

the stopover site, (3) variation in stopover patterns

between age classes (for example, if young birds stopped

less often than adults), and (4) larger variation in use

of a migration route among young birds than adults.

Given that our study sites were inland and were not

associated with major water barriers such as the Gulf

of Mexico, Pacific, and Atlantic Ocean, we suspect that

factor 4 best explains low immature and adult ratio at

our sites. Ralph (1981) found that age ratio was much

higher at his coastal study site than at an inland site

and suggested that coastal sites were peripheral to the

main migratory route of the species. Our hypothesis

that the middle Rio Grande Valley is a central migration corridor for many species is supported by the low

age ratios we detected at our banding sites if they are

interpreted in light of Ralph’s idea. To gain a better

understanding of age-dependent migration differences, individuals in both age classes would need to

be radio-tagged on their breeding grounds and tracked

over migration routes. Conducting such a study for

small songbirds using current available technology

would be difficult.

Immature and adult birds gained body mass during

daylight hours in our study. In many species, both age

classes gained more body mass than was necessary for

regular daily metabolism (but not for nocturnal migration). Based on regression analysis of capture time and

body mass, average rate of daily mass gain did not

differ between the two age classes, but we did find that

mass-gain rate in examined adults and immatures

varied by taxonomic group. For example, among 10

species of captured warblers and flycatchers, adults of

eight species (80 percent) tended to have higher rates

of daily mass gain than immatures, but among 14

species of Caradinalinae and Emberznae, adults of

only five species (36 percent) had higher rates of daily

mass gain than immatures. Migratory distance cannot explain this inconsistency between age classes

because the difference in mass gain seems independent of migratory status. Variation in molting schedules between age classes could potentially affect rates

of body mass change within species (Winker and

others 1992a), leading to species-specific patterns of

age-related mass change. But most fall migrants captured at our sites had completed molting (Finch and

Yong unpubl.), so molting schedules are unlikely to

cause age-dependent differences in rates of mass gain.

Stopover habitats were used by young and adult

Wilson’s Warbler differently during fall migration,

with young birds frequently using less inferior habitat

than adults and resuming migration more slowly

(Yong and others 1998). We suggest that such agerelated differences in habitat use may explain some of

the differences in rates of mass gain between age

groups.

Site Differences in Capture Rates

Mist-netting landbirds requires personnel who are

proficient in operating nets and handling birds. In

addition, because nets need to be checked frequently,

the technique is labor-intensive. To our knowledge,

most mist-net studies of landbird stopover ecology

operate at a single banding site or at maximum, two to

three sites in a targeted area (such as at Manomet Bird

Observatory, Long Point Bird Observatory, Point Reyes

Bird Observatory, and Migratory Bird Group of the

University of Southern Mississippi). Site selection is

typically nonrandom, a function of specific geographical locations (for example, along major migration

routes), accessibility, and targeted habitat features.

Nonrandom site selection is used even in large-scale

mist-netting programs such as MAPS (DeSante and
42 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002

Rosenberg 1998). Our study was no exception. We

selected our two sites based on three criteria: (1)

habitats were representative of those available along

the middle Rio Grande Valley of New Mexico, (2) a

historical or vested interest in research was present at

the two sites, and (3) long-term research was technically and logistically feasible at the sites.

Because our sites were not randomly selected or

replicated, our interpretations are specific to observations made at the two sites during the sampling

period. Our RGNC site lay within the city limits of

Albuquerque, NM, was dominated by cottonwoodRussian olive stands with some willows and saltcedar

clumps, was surrounded by agricultural fields and

residential areas, and was available as a narrow

riverine band adjacent to urban growth. Our BNWR

site was rural, with vegetation available across a wide

floodplain, and it contained a diversity of habitats

such as cottonwood/willow, monotypic saltcedar, willow stands along the water channels, and agricultural

fields. Given these site differences, we assumed that

habitats would be more suitable for migratory landbirds

at BNWR than at RGNC. We captured more individuals and more species at RGNC than BNWR during fall

migration, and more at BNWR than RGNC in fall. We

suggest that fall capture rates and immature/adult

ratios were higher at the urban RGNC site than the

rural site because available habitat was more restricted at RGNC, causing adult and hatching-year

migrants to concentrate in a smaller area. When

available habitats are more spread out, fewer

immatures may be captured at net sites where suitable habitat is present if inexperience has led them to

fan out into less suitable vegetation.

Other site-specific factors may also have influenced

our results. For example, more edge or suburban

species such as the American Robin, House Finch,

American Goldfinch, Pine Siskin, House Sparrow

(Passer domesticus), and European Starling (Sturnus

vulgaris) were detected at RGNC, where human disturbance was more common. In contrast, the Yellowbilled Cuckoo, a species repeatedly petitioned by conservation organizations to be listed as threatened or

endangered in the Western United States, was captured only at BNWR. Cuckoos generally prefer lowland deciduous woodlands, willow and alder thickets,

second-growth woodlands, and deserted farmlands

and orchards (Johnsgard 1986), conditions more available at BNWR than at RGNC.

Latitudinal limits of species’ distributions may also

influence relative abundance and species richness at

our middle Rio Grande sites. For example, BNWR

probably represents the northern distributional limits

of the Pyrrhuloxia, Verdin, Brown-crested Flycatcher,

and Lucy’s Warbler in the middle Rio Grande Valley.

Analyzing our wood warbler data set, Kelly and others

(1999) found that migrant abundance at our netting

sites could be predicted by summer range size and by

distance between summer ranges and study sites.

Conservation Implications

The large volume and complex species-and-age composition of landbirds migrating through our sites illustrate the value of middle Rio Grande riparian habitats

as stopover sites. Middle Rio Grande migrants appeared to accumulate small fat loads and repeatedly

replenish fat stores at stopover sites en route, rather

than accumulate large fat loads and stop infrequently.

This “short-stopover/low-fat-loads” strategy was also

reported for migratory thrushes along the northern

coast of the Gulf of Mexico after spring trans-Gulf

migration (Yong and Moore 1997). Our simulation

suggests that if birds stopped 10 times en route, taking

on fat stores equal to 5 percent of body mass at each

stop, they would be able to fly about 20 percent farther

than if they deposited the entire fat stores (50 percent

body mass) at a single stopover site (see also Yong and

Moore 1997). Migrants may apply this strategy when

north-south habitat is continuously available between

the wintering and breeding grounds, or they may

switch to this strategy when they encounter extensive

stretches of suitable habitat. When availability of

stopover sites is limited or unpredictable, the strategy

of loading large fat stores prior to undertaking long

flights may be superior (Alerstam and Lindström

1990). Migration over ecological barriers such as the

Gulf of Mexico and Sahara Deserts seems to elicit the

latter strategy in many landbird species. Deforestation and habitat deterioration caused by catastrophic

fires, exotic plant invasions, river dewatering, and

urbanization along the Rio Grande migration corridor

may reduce the number and quality of stopover sites.

If high-quality stopover sites become increasingly hard

to find along the Rio Grande, migrant success at

replenishing fat loads periodically or quickly will decrease, and it will in turn reduce the probability of

successful migration. To ensure that sufficient stopover sites remain available for landbird migration in

the Southwest, we urge that conservation and restoration of habitats and water along the middle Rio Grande

be emphasized through collaborative agreements

among the river’s diverse land managers.

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Common name, scientific name, and alpha code of landbird species captured during spring and fall

migration along the middle Rio Grande Valley of New Mexico.

Alpha

Common name Scientific name2 code3 Status4

Accipitridae

Sharp shinned Hawk Accipiter striatus SSHA B

Cooper’s Hawk Accipiter cooperii COHA B

Falconidae

American Kestrel Falco sparverius MAKE B

Columbidae

Mourning Dove Zenaida macroura MODO B

White-winged Dove Zenaida asiatica WWDO C

Cuculidae

Yellow billed Cuckoo Coccyzus americanus YBCU A

Greater Roadrunner Geococcyx californianus GRRO R

Caprimulgidae

Common Nighthawk Chordeiles minor CONI A

Alcedinidae

Belted Kingfisher Ceryle alcyon BEKI B

Picidae

Red napped Sapsucker Sphyrapicus nuchalis RNSA B

Williamson’s Sapsucker Sphyrapicus thyroideus WISA B

Downy Woodpecker Picoides pubescens DOWO R

Hairy Woodpecker Picoides villosus HAWO R

Ladder-backed Woodpecker Picoides scalaris LBWO R

Northern Flicker Colaptes auratus RSFL B

Tyrannidae

Olive sided Flycatcher Contopus borealis OSFL A

Western Wood Pewee Contopus sordidulus WEWP A

Willow Flycatcher Empidonax traillii WIFL A

Least Flycatcher Empidonax minimus LEFL A

Hammond’s Flycatcher Empidonax hammondii HAFL A

Dusky Flycatcher Empidonax oberholseri DUFL A

Gray Flycatcher Empidonax wrightii GRFL A

Western Flycatcher3 Empidonax difficilis COFL A

Black Phoebe Sayornis nigricans BLPH B

Say’s Phoebe Sayornis saya SAPH B

Ash throated Flycatcher Myiarchus cinerascens ATFL A

Brown-crested Flycatcher Myiarchus tyrannulus BCFL C

Western Kingbird Tyrannus verticalis WEKI A

Hirundinidae

Barn Swallow Hirundo rustica BARS A

Violet green Swallow Tachycineta thalassina VGSW A

Bank Swallow Riparia riparia BANS A

Northern Rough-winged Swallow Stelgidopteryx serripennis NRWS A

Corvidae

Scrub Jay Aphelocoma coerulescens SCJA R

Appendix I—Common and Scientific Names and Alpha Codes ____________
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002 47

Parid

Mountain Chickadee Poecile gambelli MOCH R

Black capped Chickadee Poecile atricapillus BCCH R

Oak Titmouse Baeolophus inornatus PITI R

Remizidae

Verdin Auriparus flaviceps VERD B

Aegithalidae

Common Bushtit Psaltriparus minimus COBU R

Sittidae

Red-breasted Nuthatch Sitta canadensis RBNU R

White-breasted Nuthatch Sitta carolinensis WBNU R

Certhiidae

Brown Creeper Certhia americana BRCR B

Troglodytidae

Rock Wren Salpinctes obsoletus ROWR B

Cactus Wren Camphlorhynchus brunneicapillus CACW C

Bewick’s Wren Thryomanes bewickii BEWR R

House Wren Troglodytes aedon HOWR A

Marsh Wren Cistothorus palustris MAWR B

Winter Wren Troglodytes troglodytes WIWR R

Carolina Wren Thryothorus ludovicianus CARW B

Muscicapidae

Sylviinae

Ruby-crowned Kinglet Regulus calendula RCKI B

Golden-crowned Kinglet Regulus satrapa GCKI R

Blue-gray Gnatcatcher Polioptila caerulea BGGN A

Turdinae

Townsend’s Solitaire Myadestes townsendi TOSO B

Swainson’s Thrush Catharus ustulatus SWTH A

Hermit Thrush Catharus guttatus HETH B

American Robin Turdus migratorius AMRO B

Mimidae

Gray Catbird Dumetella carolinensis GRCA A

Northern Mockingbird Mimus polyglottos NOMO B

Sage Thrasher Oreoscoptes montanus SATH B

Brown Thrasher Toxostoma rufum BRTH B

Crissal Thrasher Toxostoma dorsale CRTH B

Vireonidae

Bell’s Vireo Vireo bellii BEVI A

Gray Vireo Vireo vicinior GRVI A

Plumbeous Vireo Vireo plumbeous SOVI A

Warbling Vireo Vireo gilvus WAVI A

Red-eyed Vireo Vireo olivaceous REVI A

Yellow-throated Vireo Vireo flavifrons YTVI A

Emberizidae

Parulinae

Prothonotary Warbler Protonotaria citrea PROW A

Blue-winged Warbler Vermivora pinus BWWA A

Lawrence’s Warbler Vermivora pinus x V. chrysoptera LAWA A

Alpha

Common name Scientific name2 code3 Status4
48 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002

Tennessee Warbler Vermivora peregrina TEWA A

Orange-crowned Warbler Vermivora celata OCWA A

Nashville Warbler Vermivora ruficapilla NAWA A

Virginia Warbler Vermivora virginiae VIWA A

Lucy’s Warbler Vermivora luciae LUWA C

Yellow Warbler Dendroica petechia YWAR A

Magnolia Warbler Dendroica magnolia MAWA A

Chestnut-sided Warbler Dendroica pensylvanica CSWA A

Yellow-rumped Warbler Dendroica coronata YRWA B

Black-throated Gray Warbler Dendroica nigrescens BTYW A

Black-throated Blue Warbler Dendroica caerulescens BTBW A

Blackburnian Warbler Dendroica fusca BLBW A

Townsend’s Warbler Dendroica townsendi TOWA A

Prairie Warbler Dendroica discolor PRAW A

Western Palm Warbler Dendroica palmarum WPWA A

Black-and-white Warbler Mniotilta varia BAWW A

American Redstart Setophaga ruticilla AMRE A

Northern Waterthrush Seiurus noveboracensis NOWA A

Ovenbird Seiurus aurocapillus OVEN A

Kentucky Warbler Oporornis formosus KEWA A

Mourning Warbler Oporornis philadelphia MOWA A

MacGillivray’s Warbler Oporornis tolmiei MGWA A

Common Yellowthroat Geothlypis trichas COYE A

Canada Warbler Wilsonia canadensis CAWA A

Wilson’s Warbler Wilsonia pusilla WIWA A

Hooded Warbler Wilsonia citrina HOWA A

Yellow-breasted Chat Icteria virens YBCH A

Thraupinae

Scarlet Tanager Piranga ludoviciana SCTA A

Summer Tanager Piranga rubra SUTA A

Western Tanager Piranga ludoviciana WETA A

Hepatic Tanager Piranga flava HETA A

Cardinalinae

Pyrrhuloxia Cardinalis sinuatus PYRR R

Rose-breasted Grosbeak Pheucticus ludovicianus RBGR A

Black-headed Grosbeak Pheucticus melanocephalus BHGR A

Blue Grosbeak Guiraca caerulea BGGR A

Lazuli Bunting Passerina amoena LAZB A

Indigo Bunting Passerina cyanea INBU A

Painted Bunting Passerina ciris PABU A

Dicksissel Spiza americana DICK A

Emberizinae

Green-tailed Towhee Pipilo chlorurus GTTO A

Spotted Towhee Pipilo erythrophthalmus SPTO B

Canyon Towhee Pipilo fuscus CANT R

Cassin’s Sparrow Aimophila cassinii CASP B

American Tree Sparrow Spizella arborea ATSP B

Chipping Sparrow Spizella passerina CHSP A

Clay-colored Sparrow Spizella pallida CCSP A

Brewer’s Sparrow Spizella breweri BRSP A

Alpha

Common name Scientific name2 code3 Status4
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002 49

Vesper Sparrow Pooecetes gramineus VESP B

Lark Sparrow Chondestes grammacus LASP A

Black-throated Sparrow Amphispiza bilineata BTSP B

Sage Sparrow Amphispiza belli SASP B

Lark Bunting Calamospiza melanocorys LARB A

Savannah Sparrow Passerculus sandwichensis SAVS B

Grasshopper Sparrow Ammodramus savannarum GRSP A

Song Sparrow Melospiza melodia SOSP B

Lincoln’s Sparrow Melospiza lincolnii LISP A

White-throated Sparrow Zonotrichia albicollis WTSP B

Golden-crowned Sparrow Zonotrichia atricapilla GCSP B

White-crowned Sparrow Zonotrichia leucophrys WCSP B

Dark-eyed Junco Junco hyemalis DEJU B

Icterinae

Yellow-headed Blackbird Xanthocephalus xanthocephalus YHBL A

Red-winged Blackbird Agelaius phoeniceus RWBL B

Western Meadowlark Sturnella neglecta WEME B

Common Grackle Quiscalus quiscula COGR R

Great-tailed Grackle Quiscalus mexicanus GTGT R

Brown-headed Cowbird Molothrus ater BHCO B

Orchard Oriole Icterus spurius OROR A

Bullock’s Oriole Icterus galbula BUOR A

Fringillidae

Cassin’s Finch Carpodacus cassinii CAFI B

House Finch Carpodacus mexicanus HOFI B

Pine Siskin Carduelis pinus PISI B

Lesser Goldfinch Carduelis psaltria LEGO B

American Goldfinch Carduelis tristis AMGO B

White-winged Crossbill Loxia leucoptera WWCR B

Evening Grosbeak Coccothraustes vespertinus EVGO B

1

Common and scientific names are from the Check-list of North American Birds (AOU 1983) and its supplements.

2

Alpha code is from the Manual of North American Bird Banding (BBL 1984) and its supplements.

3

Status is as designated by the Partners in Flight preliminary list: A = Neotropical migrants, those that breed in North America and

spend their nonbreeding period primarily south of the United States; B = temperate migrants, those that breed and winter extensively

in North America; C = migrants whose breeding range is primarily south of the United States/Mexican border, and enter the United Sates

along the Rio Grande Valley or where the Mexican highlands extend across the United States border. These populations largely vacate

the United States during the winter months. R = permanent resident species that primarily have overlapping breeding and nonbreeding

areas. 4

No attempt was made to separate the Pacific-slope Flycatcher (Empidonax difficilis) and Cordilleran Flycatcher (E. occidentalis).

Authors suspected both species were captured during migration along the middle Rio Grande.

Alpha

Common name Scientific name2 code3 Status4
50 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002

Appendix II—Species Captured by Mist Netting ________________________

List of avian species captured by mist netting but not detected by point counts during spring and fall migration

of 1994, 1995, and 1996 along the middle Rio Grande. See appendix I for scientific names.

Species Total captures

Bell’s Vireo 1

Black-throated Blue Warbler 1

Blue-winged Warbler 1

Cactus Wren 1

Carolina Wren 1

Cassin’s Sparrow 1

Canada Warbler 1

Clay-colored Sparrow 202

Fox Sparrow 1

Golden-crowned Sparrow 1

Gray Vireo 2

Hepatic Tanager 4

Hooded Warbler 3

Lawrence’s Warbler 1

Least Flycatcher 4

Magnolia Warbler 3

Orchard Oriole 3

Painted Bunting 6

Plain Titmouse 1

Prairie Warbler 1

Prothonotary Warbler 3

Red-Eyed Vireo 2

Sage Sparrow 1

Sage Thrasher 1

Scarlet Tanager 1

Tennessee Warbler 1

Western Palm Warbler 1

White-winged Crossbill 1
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002 51

List of avian species detected during point counts but not captured by mist

netting during spring and fall migration of 1994, 1995, and 1996 along the

middle Rio Grande Valley of New Mexico.

Common name Scientific name1

Pied-billed Grebe Polilymbus podiceps

Ring-billed Gull Larus delawarenis

Franklin’s Gull Larus pipixcan

Double-crested Cormorant Phalacrocorax auritus

Olivaceous Cormorant Phalacrocorax olivaceus

American White Pelican Pelecanus erythrorhynchos

Common Merganser Mergus merganser

Mallard Anas platyrhynchos

American Black Duck Anas rubripes

Gadwall Anas strepera

American Wigeon Anas americana

American Green-winged Teal Anas crecca

Blue-winged Teal Anas discors

Cinnamon Teal Anas cyanoptera

Northern Shoveler Anas clypeata

Wood Duck Aix sponsa

Snow Goose Chen caerulescens

Greater White-fronted Goose Anser albifrons

Canada Goose Branta canadensis

White-faced Ibis Plegadis chihi

Great Blue Heron Ardea herodias

Great Egret Casmerodius albus

Snowy Egret Egretta thula

Cattle Egret Bulbulcus ibis

Green-backed Heron Butorides striatus

Black-crowned Night-heron Nycticorax nycticorax

Whooping Crane Grus americana

Sandhill Crane Grus canadensis

Virginia Rail Rallus limicola

Sora Porzana carolina

American Coot Fulica americana

Red-necked Phalarope Phasianus colchicus

American Avocet Recurvirostra americana

Common Snipe Gallinago gallinago

Western Sandpiper Calidris mauri

Greater Yellowlegs Tringa flavipes

Lesser Yellowlegs Tringa flavipes

Willet Catoptrophorus semipalmatus

Spotted Sandpiper Actitis macularia

Long-billed Curlew Numenius americanus

Killdeer Charadrius vociferus

Semipalmated Plover Charadrius semipalmatus

Scaled Quail Callipepla squamata

Gambel’s Quail Callipepla gambelii

Wild Turkey Meleagris gallopavo

Turkey Vulture Cathartes aura

Mississippi Kite Ictinia mississippiensis

Northern Harrier Circus cyaneus

Northern Goshawk Accipiter gentilis

Red-tailed Hawk Buteo jamaicensis

Swainson’s Hawk Buteo swainsoni

Gray Hawk Buteo nitidus

Golden Eagle Aquila chrysaetos

Appendix III—Species Detected Over 3-Year Study______________________
52 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-99. 2002

Bald Eagle Haliaeetus leucocephalus

Prairie Falcon Falco mexicanus

Peregrine Falcon Falco peregrinus

Merlin Falco columbarius

Osprey Pandion haliaetus

Western Screech-owl Otus kennecotti

Great Horned Owl Bubo virginianus

Burrowing Owl Athene cunicularia

Acorn Woodpecker Melanerpes formicivorus

Lewis’ Woodpecker Melanerpes lewis

Whip-poor-will Caprimulgus vociferus

Common Poor-will Phalaenoptilus nuttallii

Lesser Nighthawk Chordeiles acutipennis

Black-chinned Hummingbird Archilochus alexandri

Broad-tailed Hummingbird Selasphorus platycercus

Rufous Hummingbird Selasphorus rufus

Calliope Hummingbird Stellula calliope

Eastern Kingbird Tyrannus tyrannus

Cassin’s Kingbird Tyrannus vociferans

Vermilion Flycatcher Pyrocephalus rubinus

Horned Lark Eremophila alpestris

Black-billed Magpie Pica pica

Blue Jay Cyanocitta cristata

Steller’s Jay Cyanocitta stelleri

Common Raven Corvus corax

Chihuahuan Raven Corvus cryptoleucus

American Crow Corvus brachyrhynchos

Pinyon Jay Gymnorhinus cyanocephalus

European Starling Sturnus vulgaris

Bobolink Dolichonyx oryzivorus

Hooded Oriole Icterus cucullatus

Brewer’s Blackbird Euphagus cyanocephalus

Harris’ Sparrow Zonotrichia guerula

Mountain White-crowned Sparrow Zonotrichia leucophrys

Gambel’s White-crowned Sparrow Zonotrichia leucophrys

Chipping Sparrow Spizella passerina

Slate-colored Junco Junco hyemalis

Oregon Junco Junco hyemalis

Rufous-crowned Sparrow Aimophila ruficeps

Purple Martin Progne subis

Cliff Swallow Hirundo pyrrhonota

Tree Swallow Tachycineta bicolor

Cedar Waxwing Bombycilla cedrorum

Phainopepla Phainopepla nitens

Loggerhead Shrike Lanius ludovicianus

Myrtle Warbler Dendroica coronata

Audubon’s Warbler Dendroica coronata

Blackpoll Warbler Dendroica striata

Grace’s Warbler Dendroica graciae

Pine Warbler Dendroica pinus

Painted Redstart Myioborus pictus

House Sparrow Passer domesticus

American Pipit Anthus spinoletta

Long-billed Thrasher Toxostoma longirostre

Pygmy Nuthatch Sitta pygmaea

Eastern Bluebird Sialia sialis

Western Bluebird Sialia mexicana

Mountain Bluebird Sialia currucoides

1

Common and scientific names are from the Check-list of North American Birds (AOU 1998) and

its supplements.

Common name Scientific name1
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